Events of the Cluster
Here you can find all current events of the Cluster of Excellence.
For all past events see our ARCHIVE below.
Women in Machine Learning 2nd Workshop, October 11, 2024
Bridging Industry and Academia
2nd Workshop of the Tübingen Women in Machine Learning
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Friday, October 11, 2024 | 9 am -- 4:30 pm
Venue: Max-Planck-Insitute for Intelligent Systems, Tübingen, lecture hall ground floor
Organizers: Tübingen Women in ML (TWiML)
Registration: We kindly ask all participants to register here until October 1st.
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PROGRAM
09:00 - 11:30 | Invited talks: Betty Mohler, Amazon Science Tanja Tauber, ZEISS Corporate Research & Technology Georgia Chalvatzaki, TU Darmstadt Auguste Schultz, University of Tübingen Almut Sophia Koepke, University of Tübingen (TBC) |
11:30 - 12:30 | Science speed dating |
12:30 - 13:30 | LUNCH |
13:30 - 15:30 | Poster Session |
15:30 - 16:30 | Panel discussion |
16:30 | APERITIF |
The event is supported by WiML, Cluster of Excellence 'Machine Learning' and IMPRS-IS.
We will assign a NeurIPS travel award for the best poster.
All participants must abide the WiML CoC.
Cluster Colloquium "Machine Learning"
Seminar Series of the Cluster of Excellence "Machine Learning"
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Wednesdays | 2.00 - 3.00 pm | followed by a get-together
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PROGRAM
More information will follow shortly!
Event Archive
Here you can find all past Cluster events.
Cluster Colloquium "Machine Learning" - 1° Wednesday of the month
Seminar Series of the Cluster for Excellence "Machine Learning"
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Wednesday, 2:00 - 3:00 pm, followed by get-together
Lecture hall, AI Research Building, Maria von Linden-Str. 6 (ground floor), 72076 Tübingen
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PROGRAM
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16.11.2023 Neural Flow Operators: Learning dynamical systems from data
(and what you can do with them)
Prof. Daniel DURSTEWITZ
Central Institute of Mental Health (CIMH)
Professor in Theoretical Neuroscience / Department Head WebsiteMost systems we encounter in science and engineering, at one level or the other could be cast in the form of systems of differential equations, whose short- and long-term behavior is the subject of dynamical systems (DS) theory. Can we infer generative DS models directly from time series observations without explicit knowledge of the underlying governing equations? In my talk I will first provide some necessary background in DS theory, and then discuss recent deep learning approaches for approximating the flow of unknown DS from data. I will in particular focus on challenges in training, recent teacher forcing techniques to overcome these, and how to map systems evolving on multiple time scales and observed through multiple data modalities. I will also discuss how to analyze and interpret trained systems. Concepts will be illustrated on various examples from neuroscience with DS models trained to recreate neurophysiological recordings.
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01.03.2023 Reverse-engineering Drosophila action selection and movement control
Prof. Pavan Ramdya
Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
Director of the Neuroengineering Laboratory WebsiteA shared goal of neuroscience and robotics is to understand how systems can be built to move effectively and autonomously through the world. However, state-of-the-art algorithms for selecting and executing limb behaviors in robots are still quite primitive compared with those used by animals. To inform the design of artificial systems, we are investigating how the fly, Drosophila melanogaster, selects and controls its behaviors and how this process can be modulated by learning. I will discuss how we are pursuing this research program using a combination of machine learning, microscopy, and modeling.
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26.10.2022 Machine learning for improved understanding and projections of climate change
Veronika Eyring
Head of Earth System Model Evaluation and Analysis Department at the German Aerospace Center
(Deutsches Zentrum für Luft- und Raumfahrt) and Professor and Chair of Climate Modeling at the
University of Bremen. WebsiteClimate models are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but considerable biases and uncertainties in their projections remain. A large contribution to this uncertainty stems from differences in the representation of phenomena such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. High-resolution, cloud resolving models with horizontal resolution of a few kilometers alleviate many biases of coarse-resolution models for deep clouds and convection, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. In this talk I will present work from my group and collaborators where we use short regional and global cloud resolving simulations to develop machine learning based atmospheric parametrizations for a reduction of long-standing biases in climate models. While unconstrained neural networks often learn spurious relationships that can lead to instabilities in climate simulations, causally informed deep learning can mitigate this problem by identifying direct physical drivers of subgrid-scale processes. Trust and generalizability of the ML models can be further improved by introducing climate invariant variables, physical constraints, or equation discovery. Our approach can drive a paradigm shift in current climate and Earth system modelling towards a new data-driven, yet still physics-aware, ML-based hybrid climate model for improved understanding and projections of climate change.
About Veronika Eyring
Veronika Eyring is Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She maintains a strong collaboration with the National Center for Atmospheric Research (NCAR, USA) as Affiliate Scientist, with the DLR Causal Inference Group in Jena that she founded in 2017, and with the team of the European Research Council (ERC) Synergy Grant on "Understanding and Modelling the Earth System with Machine Learning (USMILE)". Veronika's research focuses on understanding and modelling the Earth system with machine learning to improve climate projections and technology assessments for actionable climate science.
She has extensive experience in the coordination of large international research projects, e.g. in her role as Chair of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP) Panel (2014-2020). She has authored many peer-reviewed journal articles and has contributed to the Intergovernmental Panel on Climate Change (IPCC) climate assessments since many years, including her role as Coordinating Lead Author for Chapter 3 “Human influence on the climate system” in the IPCC Sixth Assessment Report of Working Group I. Veronika is Fellow of the European Lab for Learning & Intelligent Systems (ELLIS), Member of the Scientific Advisory Board of Worldfund, and actively involved in WCRP activities for many years. Veronika received the Gottfried Wilhelm Leibniz Prize in 2021 for her significant contributions to improving the understanding and accuracy of climate projections through process-oriented modeling and model evaluation.
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18.05.2022 Learning compositional, structured, and interpretable models of the world
Hedvig Kjellström
Professor in the Division of Robotics, Perception and Learning, Department of Intelligent Systems,
at KTH Royal Institute of Technology, Sweden, and Principal AI Scientist at Silo AI, Sweden. WebsiteDespite their fantastic achievements in fields such as computer vision and natural language processing, state-of-the-art deep learning approaches differ from human cognition in fundamental ways. While humans can learn new concepts from just a single or few examples, and effortlessly extrapolate new knowledge from concepts learned in other contexts, deep learning methods generally rely on large amounts of data for their learning. Moreover, while humans can make use of contextual knowledge of e.g. laws of nature and insights into how others reason, such information is generally hard to exploit in deep learning methods.
Current deep learning approaches are indeed purposeful for a wide range of applications where there are large volumes of training data and/or well defined problem settings. However, models that learn in a more human-like manner have the potential to be more adaptable to new situations, be more data efficient and also more interpretable to humans - a desirable property e.g. for intelligence augmentation applications with a human in the loop, e.g. medical decision support systems or social robots.
In this talk I will describe a number of projects in my group where we explore disentanglement, temporality, multimodality, and cause-effect representations to accomplish compositional, structured, and interpretable models of the world.
About Hedvig Kjellström
Hedvig Kjellström is a Professor in the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology, Sweden. She is also a Principal AI Scientist at Silo AI, Sweden and an affiliated researcher in the Max Planck Institute for Intelligent Systems, Germany. She received an MSc in Engineering Physics and a PhD in Computer Science from KTH in 1997 and 2001, respectively, and thereafter worked at the Swedish Defence Research Agency, before returning to a faculty position at KTH. Her present research focuses on methods for enabling artificial agents to interpret human and animal behavior. These ideas are applied in the study of human aesthetic bodily expressions such as in music and dance, modeling and interpreting human communicative behavior, the understanding of animal behavior and cognition, and intelligence amplification - AI systems that collaborate with and help humans.Hedvig has received several prizes for her research, including the 2010 Koenderink Prize for fundamental contributions in computer vision. She has written around 130 papers in the fields of computer vision, machine learning, robotics, information fusion, cognitive science, speech, and human-computer interaction. She is mostly active within computer vision, where she is an Associate Editor for IEEE TPAMI and regularly serves as Area Chair for the major conferences.
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01.12.2021 Beyond the First Portrait of a Black Hole
Katherine L. (Katie) Bouman
Assistant Professor of Computing and Mathematical Sciences, Electrical Engineering and Astronomy at
California Institute of Technology Pasadena, CA, USA. Website
As imaging requirements become more demanding, we must rely on increasingly sparse and/or noisy measurements that fail to paint a complete picture. Computational imaging pipelines, which replace optics with computation, have enabled image formation in situations that are impossible for conventional optical imaging. For instance, the first black hole image, published in 2019, was only made possible through the development of computational imaging pipelines that worked alongside an Earth-sized distributed telescope. However, remaining scientific questions motivate us to improve this computational telescope to see black hole phenomena still invisible to us and to meaningfully interpret the collected data. This talk will discuss how we are leveraging and building upon recent advances in machine learning in order to achieve more efficient uncertainty quantification of reconstructed images as well as to develop techniques that allow us to extract the evolving structure of our own Milky Way's black hole over the course of a night.
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06.10.2021 Employing Immersive Virtual Reality and Machine Learning to Reveal Geometric Principles
of Individual and Collective Decision-Making
Iain Couzin
Director of the Max Planck Institute of Animal Behavior, Department of Collective Behaviour
and the Chair of Biodiversity and Collective Behaviour at the University of Konstanz Webseite
I will discuss our development and application of new machine learning technologies for automated tracking and identification of unmarked individuals, computational sensory reconstruction, bio-mimetic robotics, and ‘holographic’ virtual reality (VR) for animals, and demonstrate how these quantitative methodologies provide new insights into individual and collective sensing and decision-making. For example, they allow us to reconstruct (automatically) the dynamic, time-varying sensory networks in large animal collectives, to identify at any instant in time the most socially-influential individuals within groups, and to predict cascades of social contagion (behavioural change) before they actually occur. Furthermore, such methodologies enable us to study phenomena across multiple scales of biological organisation, from neural interactions to individual and collectives decision-making. Specifically, we have recently investigated how animals choose among spatially-discrete options, a central challenge in their lives. Employing an integrated theoretical and experimental approach (using immersive VR), with both invertebrate and vertebrate models—the fruit fly, desert locust and zebrafish—we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (critical) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.
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07.07.2021 Beyond Expectations & Better than Average: Foundations of Machine Learning Systems
Robert Williamson
Professorship for 'Foundations of Machine Learning Systems'
at our Cluster of Excellence "Machine Learning"
Machine Learning (ML) technology provides value by its integration into a system, either technological or socio-technical. This requires an external outward-looking view of ML, rather than the usual internal inward-looking view of much ML research. I will illustrate the external view by considering some foundational concerns regarding ML which motivates the research I propose to conduct in Tübingen. The starting point turns out to be a more careful analysis of expectation or averages. Somewhat astonishingly, there is a lot to be said, and a lot to be yet discovered, about this apparently elementary building block of ML systems, and about its natural generalisations. I will argue why one should consider moving beyond expectations, and explore some of the intriguing technical problems that naturally arise. I will also explain how replacing expectation by something else offers an attractive alternate approach to questions of fairness in ML, but how this choice, like other approaches, is inherently value-laden in a strong sense.
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09.06.2021 Learning from Trees - Modeling, Simulation, and Inference of Microbial Genome Evolution
Franz Baumdicker
Head of the Independent Research Group 'Mathematical and Computational Population
Genetics', a joint group of Tübingen's Excellence Clusters "Controlling Microbes to Fight
Infections " and "Machine Learning"
Population genetics aims to understand how the observed genetic diversity emerged. In population genetics, many theoretical results have been developed in times where not much genomic and genetic data were available. These theory-driven results are still essential for our research, but data-driven discoveries have meanwhile dramatically changed our view of evolution and ecology, in particular for bacteria.
The vast amount of newly sequenced genetic data leads to a multitude of interesting applications in the emerging field of machine learning in population genetics. The main challenge is that sequence data are not independent of one another, but rather are linked by their phylogenetic relationship, often represented by a tree sequence. Thus independent training data generation relies heavily on simulation procedures. I will present some of our approaches to develop, analyze, and apply supervised machine learning tools that can use this phylogenetic relationship to improve our understanding of bacterial genome evolution.
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14.04.2021 Machine learning for medical image analysis and why clinicians are not using it
Christian Baumgartner
Head of the Independent Research Group 'Machine Learning in Medical Image Analysis' at our
Cluster of Excellence "Machine Learning"
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03.03.2021 Challenges in Renewable Energy Systems: A (mostly) Probabilistic Perspective
Nicole Ludwig
Head of the Early Career Research Group 'Machine Learning in Sustainable Energy Systems
(MSES)' at our Cluster of Excellence "Machine Learning"
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03.02.2021 Scalable Bayesian Inference: New Tools for New Challenges
Robert Bamler
Professorship for "Data Science and Machine Learning"
at our Cluster of Excellence "Machine Learning"
Abstract
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13.01.2021 Generalizing from sparse data and learning from other people - Charley Wu
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Head of the Independent Research Group "Human and Machine Cognition"
at our Cluster of Excellence "Machine Learning"
Abstract
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02.12.2020 Morals and Methodology - Konstantin Genin
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Head of the Independent Research Group "Epistemology and Ethics of Machine Learning"
at our Cluster of Excellence "Machine Learning"
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14.10.2020 CANCELED
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01.04.2020 CANCELLED DUE TO CORONA CRISIS
How to be fair - The concept of fairness from a Computational Social Choice perspective
Britta Dorn (Host: Fabian Sinz)
Mathematical Structures in Computer Science, Department of Computer Science,
University of Tübingen. Webpage
Abstract
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06.05.2020 CANCELLED DUE TO CORONA CRISIS
Bob Williamson (Host: Ulrike von Luxburg)
Distinguished Researcher in Data61 and Professor at Research School of Computer Science,
Australian National University, Canberra
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04.03.2020 Ian Couzin -- CANCELLED
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05.02.2020 Learning and artificial intelligence in the quantum domain
Hans Briegel (Host: Eric Raidl)
Institute for Theoretical Physics, University Innsbruck & Department of Philosophy,
University Konstanz Webpage
Abstract
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08.01.2020 Mining observations and climate models for detecting and attributing anthropogenic
climate change in the world’s water cycle
Lukas Gudmundsson (Host: Fabian Sinz)
Institute for Atmospheric and Climate Science, ETH Zürich Webpage
Abstract
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04.12.2019 Explaining and Representing Novel Concepts With Minimal Supervision
Zeynep Akata (Host: Fabian Sinz)
Cluster of Excellence "Machine Learning", Explainable Machine Learning, University Tübingen
Webpage
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21.10.2019 Biomarker Discovery in Clinical Time Series
Karsten Borgwardt (Host: Fabian Sinz)
Department of Biosystems Science and Engineering, ETH Zuerich. Webpage
Abstract
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06.11.2019 Gaussian Process emulation of tsunami and climate models
Serge Guillas (Host: Motonobo Kanagawa)
University College London / The Alan Turing Institute, UK. Webpage
Abstract
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03.07.2019 Machine Learning, Neuroscience, and Spiking Neural Networks
Robert Legenstein (Host: Harald Baayen)
Inst. for Theoretical Computer Science, Graz University of Technology, Austria
Webpage
Abstract
2024
Philosophy of Science Meets Machine Learning (PhilML'2024), September 11 - 13, 2024
Philosophy of Science Meets Machine Learning (PhilML'2024)
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September 11 - 13, 2024
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen
Organizers: Markus Ahlers, Raysa Benatti, Heather Champion, Timo Freiesleben, Konstantin Genin, Thomas Grote, Sebastian Zezulka
Registration: Online registartion here.
Please note: there is a modest registration fee.
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Machine learning methods have become a mainstay in the toolkit of various scientific disciplines. For the fourth time, the PhilML'24 conference offers the opportunity to explore how recent developments in machine learning change the process of scientific research. The main conference will take place from September 11-13 and will be preceded by a one-day graduate workshop on September 10. We invite submissions of extended abstracts both for the conference and the graduate workshop.
The PhilML conference and workshop are organized jointly by the Ethics and Philosophy Lab and the Epistemology and Ethics of Machine Learning group at the Tübingen Cluster of Excellence "Machine Learning".
The workshop’s central topics include
- Reflections on key topics such as learning, reliability, causal inference, robustness, explanation, trust, transparency, and understanding.
- Implications of machine learning for the sciences, e.g. physics, cognitive science, biology, psychology, social science, or medicine.
- Implications of machine learning for scientific methodology, e.g. model-building and model selection, design of experiments, conceptual engineering.
- Issues arising at the intersection of machine learning and public policy, e.g. risk assessment, resource allocation, climate and energy policy, and the provision of public services.
- Novel considerations raised by foundation models e.g., authorship, latent representation, or nativism/empiricism.
| Program |
Wednesday, September 11
08:45 - 09:00 | Arrival & Registration |
09:00 - 09:15 | Welcome |
09:15 - 10:00 | Standards for belief representation in LLMs Daniel Herrmann (University of Groningen) |
10:15 - 11:00 | Dissecting link uncertainty Sara Pernille Jensen (University of Oslo) |
11:15 - 12:00 | Do large language models have a duty to tell the truth? Brent Mittelstadt (University of Oxford) |
12:00 - 13:00 | Lunch |
13:00 - 13:45 | Causal Inference from competing treatments Ana-Andreea Stoica (MPI Tübingen) |
14:00 - 14:45 | Epistemic Virtues in Unsupervised Learning Maximilian Noichl (University of Vienna) |
14:45 - 15:30 | Coffe Break |
15:30 - 16:15 | Scrutinizing the foundations: could large language models be solipsistic Andreea Eșanu (New Europe College) |
16:30 - 17:15 | Monocultures of knowing in science & society Molly Crockett (Princeton University) |
19:00 | Conference Dinner at Freistil (covered) |
Thursday, September 12
09:00 - 09:15 | Morning Coffee |
09:15 - 10:00 | Measuring for moral performance in foundation models Julia Haas (DeepMind) |
10:15 - 11:00 | Understanding without understanding Frauke Stoll & Annika Schuster (TU Dortmund) |
11:15 - 12:00 | Conceptual Engagement in Machine Learning: Operationalism in Social Science Applications Donal Khosrowi (Leibniz University Hannover) |
12:15 - 13:00 | Lunch |
13:00 - 13:45 | What is overfitting? Nico Formanek (University Stuttgart) |
14:00 - 14:45 | Transparency of what? Hanseul Lee & Hyundeuk Cheon (Seoul National University) |
14:45 - 15:30 | Coffee Break |
15:30 - 16:15 | Thick descriptions in data-driven psychiatry Hanna van Loo & Jan Wilhelm Romeijn (University of Groningen) |
16:30 - 17:15 | Evaluating the quality of explanations beyond fidelity Stefan Buijsman (TU Delft) |
19:00 | Dinner at El Pecado (self-pay) |
Friday, September 13
09:00 - 09:15 | Morning Coffee |
09:15 - 10:00 | Precarious accurate predictions Gabrielle Johnson (Claremont McKenna College) |
10:15 - 11:00 | Values in machine learning: What follows from underdetermination? Tom Sterkenburg (LMU Munich) |
11:15 - 12:00 | Causal agnosticism about race Alexander Tolbert (Emory University) |
12:15 - 13:00 | Lunch |
13:00 - 13:45 | Does personalized allocation make our experimental designs more fair? Bertille Picard (Aix-Marseille University) |
14:00 - 14:45 | Alignment as a principal-agent problem Aydin Mohseni (Carnegie Mellon University) |
14:45 - 15:30 | Coffee Break |
15:30 - 16:15 | All causal DAGs are wrong but some are useful Dominik Janzing (Amazon Research) |
16:15 - 16:45 | Closing remarks, followed by drinks |
More information on the event website.
6th Annual Conference "Machine Learning in Science", July 9 + 10, 2024
6th Cluster Conference
"Machine Learning in Science" 2024
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► Tuesday, July 9
► Wednesday, July 10
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Venue: Neckawa (Freistil), Wöhrdstraße 25, 72072 Tübingen
Please note: Registration is closed.
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PROGRAM
All times are provided in CEST, Central European Summer Time.
Tuesday, July 9, 2024
09:00 | Ulrike von Luxburg, Philipp Berens ► Opening Remarks |
09:15 | Tobias Hauser ► Using Computational Models to Understand Neural Mechanisms Underlying Mental Disorders |
10:00 | Kerstin Ritter ► Machine Learning in Clinical Brain Research |
10:45 | Coffee Break |
11:15 | Aseem Behl ► Machine Learning Applications in Marketing Science |
11:45 | Çağatay Yildiz ► Lifelong Learning and Language Models |
12:15 | Thomas Küstner ► Machine Learning in Clinical Workflows of Medical Imaging |
13:00 | Lunch |
14:15 | Jakob Macke ► Mechanistic Models of Neural Computations |
14:45 | Alina Wernick ► AI, Law and Society - in Search of Blind Spots of AI Regulation |
15:45 | Group Photo |
16:00 | Poster Session - Cluster Projects & AIMS Fellows ► Coffee |
19:00 | Dinner |
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Wednesday, July 10, 2024
09:00 | Moritz Hardt ► The Emerging Science of Benchmarks |
09:45 | Katharina Eggensperger ► Automated Machine Learning for Science |
10:15 | Markus Ahlers ►Ethics and Evidence |
10:45 | Coffee Break |
11:15 | Keynote: Stefanie Jegelka ► Strengthening Graph Neural Networks, with Applications to Mass Spectrometry |
12:15 | Stephan Eckstein ► Quantifying Differences Between Probabilistic Causal Models |
13:00 | Ulrike von Luxburg, Philipp Berens ► Closing Remarks |
13:15 | Lunch |
14:15 - 15:30 | General Assembly (Cluster member groups only) |
Healthy Minds in Academia – April to July 2024
Healthy Minds in Academia
The talk and workshop series Healthy Minds leads you through interactive sessions and expert talks presenting tools and knowledge to learn together how to better take care of our mental health in academia and beyond. It will take place regularly over the upcoming months and is funded by the Cluster of Excellence and the AI Center.
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How can I boost my mental health? A resource oriented approach.
Wednesday, March 27, 2024 at 1:30pm
Venue: Zoom
Speaker: Suzanne Jones
This workshop offers you the chance to reflect on your inner and outer resources. You will find suggestions on how to re-load your personal battery and strengthen your mental health through positive emotions and an optimistic focus.
Suzanne Jones is a psychologist and systemic therapist and a counsellor for health promotion and stress prevention: http://www.suzanne-jones.de
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Stress management skills - Increasing mental health and well-being.
Wednesday, April 24, 2024 at 12.30pm
Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen und Zoom
Speaker: Fanny Kählke
The workshop provides an overview of strategies to promote positive mental health under stressful work conditions. You will learn about different (instrumental, regenerative, and mental) ways to cope with stress and reflect on your own coping strategies and future needs.
Mehr Informationen hier.
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Study psychologically fit.
Wednesday, May 29, 2024 at 1.30pm
Venue: Zoom
Organizers: Beate Schulze and Hendrik Hutthoff
In the two hours of the workshop, students can expect an authentic, intensive forum in which psychological crises can be discussed. In the last half hour, students can ask any questions they have about the topic.
More information here.
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Decolonizing Academia - How (not) to Fail a Decolonial Lecture Series
Wednesday, June 26, 2024 at 1.30pm
Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen und Zoom
Organizer: Sharon Nathan
Mehr Informationen here.
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Workshop: Intercultural and diversity competences and inclusion in diverse, interdisciplinary research groups
Wednesday, July 24, 2024 at 1.30pm
Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen and Zoom
Organizer: Imke Lode
Imke Lode is a certified trainer for intercultural competences, see https://lindengruen.de/dr-imke-lode/.
More information here.
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Symposium "Machine Learning in Science", May 13, 2024
Symposium "Machine Learning in Science"
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Monday, May 13, 2024 at 12.30 - 5.30pm
Veranstaltungsort: AI Research Building, Maria von Linden Str. 6, 72076 Tübingen (Lecture Hall, Ground Floor)
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12:30 | Covering Multiple Scales: Graph Machine Learning for Science Prof. Dr. Stephan Günnemann (Technical University of Munich, Germany) |
13:30 | Using DNA Language Models to Understand Different Layers of Code in Genomes |
14:30 | Coffee Break |
15:00 | On How to Incorporate Spacetime Symmetries into Your Neural Networks |
16:00 | Towards an Artificial Muse for New Ideas in Science |
17:00 | Coffee Break |
17:30 | Reinforcement Learning Theory towards Robust Discovery in Science |
Healthy Minds in Academia – Kick-off event, March 26, 2024
Healthy Minds in Academia
Kick-off event
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Tuesday, March 26, 2024 at 3pm
Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen
Organizers: Nina Effenberger, Janne Lappalainen
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The talk and workshop series Healthy Minds leads you through interactive sessions and expert talks presenting tools and knowledge to learn together how to better take care of our mental health in academia and beyond. It will take place regularly over the upcoming months and is funded by the Cluster of Excellence and the AI Center.
In this kick-off event, two PhD students will present research on the mental health situation of PhD students at the University of Tübingen itself! In addition, representatives of the mental health support from the AI Center and the psychosocial counseling of the university will introduce themselves as persons of contact. We will present the goals of our talk series and conclude with an outlook on the next events before we invite you to snacks and drinks!
This event is targeted towards employees at the University of Tübingen and the MPIs—including PIs, postdocs, PhD students, and administrative staff.
Women in Machine Learning Workshop, March 8, 2024
Machine Learning from Theory to Application
Workshop for International Women's Day
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Friday, March 8, 2024 | 9am - 1.30pm
Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen
Organizers: Tübingen Women in ML (TWiML)
Registration: We kindly ask all participants to register here.
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Program
09:00 | Arrival and Welcome |
09:00 - 09:05 | Introduction Claire Vernade, Cluster of Excellence |
09:05 - 09:45 | Caterina De Bacco, MPI-IS Probabilistic generative models for hypergraphs: structure and inference |
09:45 - 10:15 | Albane Ruaud, Cluster of Excellence Modelling Bacterial Communities with Graph Neural Networks |
10:15 - 10:50 | Coffee Break |
10:50 - 11:30 | Ulrike von Luxburg, Cluster of Excellence Theoretical guarantees for explainable machine learning |
11:30 - 12:00 | Ana-Andreea Stoica, MPI-IS Algorithm design for social good: fair design and strategic interactions |
12:00 - 13:00 | Lunch |
13:00 - 13:30 | Panel discussion: Ask me anything! |
2023
Workshop on Bandits and Statistical Tests, November 23-24, 2023
Workshop on Bandits and Statistical Tests
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Thursday, November 23 & Friday, November 24, 2023
Venue: Neues Palais, Am Neuen Palais 10, 14469 Potsdam
Hosts: Claire Vernade (Cluster Machine Learning, Tübingen) and Alexandra Carpentie (Maths department, University of Potsdam)
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We are pleased to invite you to our upcoming Workshop on Statistical Tests in Machine Learning, with a specific emphasis on Online Learning and Bandit Algorithms. The workshop aims to provide participants with a comprehensive understanding of statistical testing methodologies in the context of machine learning, particularly in the dynamic and ever-changing landscape of online and bandit settings.
This workshop is intended for researchers, practitioners, and students interested in the intersection of machine learning, statistics, and algorithm design.
Registration is closed now.
Tutorials:
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Bandit algorithms and testing: Prof. Aurelien Garivier, ENS Lyon, France
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Minimax tests: Dr. Nicolas Verzelen, INRAE, Montpellier, France
Keynote speakers:
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Prof. Thomas Berrett, University of Warwick
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Prof. Rui Castro, TU Eindhoven
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Prof. Cristina Butucea, CNRS, France
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Ariane Marandon, Sorbonne University, France
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Prof. Peter Grünwald, CWI and Leiden University, Amsterdam
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Dr. Remy Degenne, INRIA, Lille, France
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Dr. Emilie Kaufmann, CNRS, INRIA, Lille, France
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Prof. David Preinerstorfer, University of St Gallen, Switzerland
For more Information, please check here.
Art exhibit IN-ML-OUT, October 27, 2023
Presenting our art exhibit
IN-ML-OUT - Wind Energy and Machine Learning
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Friday October 27, 2023 at 5:00 pm
Venue: swt-KulturWerk (Werkstraße 4, 72074 Tübingen)
Admission free.
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Machine learning can be used to make the best possible use of wind energy in times of climate change. Nina Effenberger and Nicole Ludwig, who are engaged in research on sustainable energies in the Cluster of Excellence, will demonstrate how this can be achieved on October 27, 2023 at 5 pm in Tübingen's swt-KulturWerk: They want to use the interactive art exhibit "IN-ML-OUT " to advance the dialog between research, society and politics and demonstrate the potential of machine learning. The exhibit allows visitors to experience that our actions influence the climate, which solutions researchers can support with the help of machine learning, and which initiatives and projects on renewable energies already exist.
Following the exhibit presentation, there will be a discussion on the question “How can AI support the transformation of energy systems?”. Nicole Ludwig will participate as well as Peter Seimer (Spokesperson for Digitization, Green Party in the State Parliament of Baden-Württemberg) and Philipp Staudt (Digitized Energy Systems, University of Oldenburg). It will be moderated by Olaf Kramer (Research Center for Science Communication, University of Tübingen).
More information
AITE Closing Conference, October 24 - 26, 2023
"Artificial Intelligence, Trustworthiness and Explainability"
Closing Conference
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Tuesday October 24 to Thursday October 26, 2023
Venue: Max-Planck-Institute for Intelligent Systems (lecture hall, ground floor), Max-Planck-Ring 4, 72076 Tübingen
Organizers: Saeedeh Babaii, Sara Blanco, Oliver Buchholz, Eric Raidl
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The project „Artificial Intelligence, Trustworthiness and Explainability (AITE)“, funded by the Baden-Württemberg Stiftung, is a joint project of our Cluster "Ethics and Philosophy Lab" and the "International Centre for Ethics in the Sciences and Humanities" (IZEW) at the University of Tübingen.
Presently, it remains opaque why machine learning systems (ML) decide or answer as they do. When an image classifier says "this is a train", does it 'recognise' the train or only the rails, or something totally different? How can we be sure that it decides as it does for the right reasons? This problem is at the heart of at least two debates: Can we trust artificial intelligent (AI) systems? And if so, on which basis? Would an explanation of the decision help our understanding and ultimately foster trust? And if so, what kind of explanation? These are the central questions being adressed in the AITE project.
At the AITE closing conference scientists working on this project present their work, exchange with experts in the field and other interested scholars, and engage with the wider public in a panel discussion.
More information on the conference website
Panel discussion „Who regulates AI?“, October 20, 2023
Panel discussion
„Who regulates AI?"
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Friday October 20, 2023, at 08:00 pm
Venue: Uhlandsaal der Museumsgesellschaft, Wilhelmstr. 3, Tübingen
Admission is free.
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On Friday, October 20, at 8 pm, the Stadtmuseum Tübingen and our University will host a panel discussion on the question "Who regulates AI?".
The four panelists are all members of our cluster: Moritz Hardt, Ulrike von Luxburg, Michèle Finck and Carsten Eickhoff. The panel discussion will be moderated by SWR.
Philosophy of Science Meets Machine Learning (PhilML2023), September 12 - 14, 2023
Philosophy of Science Meets Machine Learning (PhilML2023)
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September 12-14, 2023
Location: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen
Organizers: Timo Freiesleben, Konstantin Genin, Thomas Grote, Sebastian Zezulka
Registration: We kindly ask all participants and speakers to register by 31.07.2023.
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Machine learning methods have become a mainstay in the tool-kit of various scientific disciplines. PhilML’23 offers an opportunity to explore whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this purpose, it sets out to analyse the field of machine learning through the lens of philosophy of science, including cognate fields such as epistemology and ethics. In addition, we are also interested in contributions from machine learning researchers/scientists, addressing foundational issues of their research. Similar to the previous workshops, we try to bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers.
The workshop`s central topics are:
(i) A critical reflection on key-concepts, such as ‘learning’, ‘causal inference’, ‘robustness’, ‘explanation’ or ‘understanding’.
(ii) The implications of machine learning for the special sciences, e.g. cognitive science, biology, social science or medicine.
(iii) The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers, ethical issues in model evaluation, or issues at the intersection of science and policy.
(iv) Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.
| Program |
Tuesday, September 12
09:15 - 09:30 | Arrival & Registration |
09:45 - 10:00 | Welcome |
10:00 - 10:45 | Deep Learning Robustness for Scientific Discovery: The Case of Anomaly Detection Florian Boge (TU Dortmund) |
11:00 - 11:45 | Generalizing Philosophy of Science to Unseen Instances Mel Andrews (CMU/Cincinnati) |
11:45 - 13:00 | Lunch |
13:00 - 13:45 | Recommender Systems and Inferred Preferences: Accurate or Rational? Silvia Milano (Uni Exeter) |
14:00 - 14:45 | Trustworthiness and Knowledge from Machine Learning Jonathan Vandenburg (Stanford) |
14:45 - 15:30 | Coffee Break |
15:30 - 16:15 | Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments Michael Knaus (Uni Tübingen) |
16:30 - 17:15 | Prediction, Explanation, and AI in Scientific Practice Marta Halina (Uni Cambridge) |
19:00 | Dinner |
Wednesday, Sept 13
09:00 - 09:15 | Welcome |
09:15 - 10:00 | Manipulative Explanations in AI Atoosa Kasirzadeh (Uni Edinburgh) |
10:15 - 11:00 | The Epistemology of Statistical Learning Tom Sterkenburg (LMU) |
11:15 - 12:00 | Conceptual Engagement in Machine Learning: Operationalism in Social Science Applications Alex Mussgnug (Uni Edinburgh) |
12:15 - 13:00 | Lunch |
13:00 - 13:45 | What Counts as Good Data? Kino Zhao (Simon Fraser University) |
14:00 - 14:45 | The Language of Mathematics: Epistemological Consequences of the Application of Neural Models to Mathematical Knowledge Juan Luis Gastaldi (ETH) |
14:45 - 15:30 | Coffee Break |
15:30 - 16:15 | Lifelong Statistical Testing Claire Vernade (Uni Tübingen) |
16:30 - 17:15 | Using Machine Learning to Increase Equity in Healthcare and Public Health Emma Pierson (Cornell) |
19:00 | Dinner |
Thursday, Sept 14
09:00 - 09:15 | Welcome |
09:15 - 10:00 | Why do large language models align with human brains? Mariya Toneva (MPI Saarbrücken) |
10:15 - 11:00 | Why is Homogenization Bad? Katie Creel (Northeastern) |
11:15 - 12:00 | The Function of Replicability in Machine Learning Tianqui Kou (Penn State) |
12:15 - 13:00 | Lunch |
13:00 - 13:45 | Interpersonal Comparisons of Utility: A Conventional Take Daniel Alexander Herrmann (UC Irvine) |
14:00 - 14:45 | Representational Similarity Analysis Underdetermines Similarity of Object Recognition Mechanisms in Deep Neural Networks and the Brain Bojana Grujičić (MPSCog, HU, UCL) |
15:00 - 15:45 | Learning an Index of Economic Complexity Frederik Eberhardt (Caltech) |
15:45 - 16:00 | Closing remarks, followed by drinks |
For more information, please check out the event website.
5th Annual Conference "Machine Learning in Science", July 11 + 12, 2023
5th Cluster Conference
"Machine Learning in Science" 2023
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► Tuesday, July 11 | 9am - 7pm |
► Wednesday, July 12 | 9am - 5pm |
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Venue: Freistil, Wöhrdstraße 25, 72072 Tübingen
For those who cannot attend the conference in person, the presentations will be broadcast on
Zoom (identification code: 145437).
Please note: (1) Registration with full name only. (2) It will not be possible to ask questions via Zoom.
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PROGRAM
All times are provided in CEST, Central European Summer Time.
Tuesday, July 11, 2023
09:00 | Ulrike von Luxburg, Philipp Berens ► Opening Remarks |
09:15 | Tobias Kaufmann ► Computational psychiatry: what can we learn from large-scale brain imaging data? |
10:00 | Michael Knaus ► Machine learning to predict effects in economics and beyond |
10:45 | Coffee Break |
11:15 | Claire Vernade ► Lifelong Statistical Testing |
12:00 | Christian Igel ► KEYNOTE: Machine Learning for Large-Scale Ecosystem Monitoring |
13:00 | Lunch Break |
14:00 | Poster Session and Coffee - Cluster Individual Projects and Research Groups |
16:00 | Wieland Brendel ► Machine Vision for Flexible and Robust Autonomous Robots |
16:45 | Daniela Doneva ► ML as a Tool in Gravitational Wave Physics |
17:30 | Open Space ► 18:00 Guided Tour AI Makerspace |
19:00 | Dinner |
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Mittwoch, 12. Juli 2023
09:00 | Georg Martius ► Model-based Reinforcement Learning |
09:45 | Christoph Bareither, Libuše Hannah Vepřek ► Human-ML Assemblages in the Sciences: Approaches from Digital Anthropology & STS |
10:30 | Nicole Ludwig ► Machine Learning for Sustainability - Bridging the Gap between Theory and Application |
11:00 | Coffee Break |
11:30 | Carsten Eickhoff ► Health Natural Language Processing |
12:15 | Albane Ruaud ► Modelling bacterial communities' dynamics with GNN |
13:00 | Lunch |
14:00 | Poster Session and Coffee - Cluster Network Projects ► Machine Learning in Education |
16:45 | Ulrike von Luxburg, Philipp Berens ► Closing Remarks |
Talk by Dale Durran - June 28, 2023
| Weather forecasting and climate simulation:
What are the challenges for AI and numerical weather prediction |
Dale Durran
Professor of Atmospheric Sciences
University of Washington, Seattle, USA
WHEN: Wednesday, 28.06.2023 at 2:00 pm, followed by a get-together
WHERE: Lecture hall, AI Research Building, Maria von Linden-Str. 6 (ground floor), 72076 Tübingen
HOSTS: Martin Butz und Matthias Karlbauer
Talk by Srinivas Turaga - June 21, 2023
| How to simulate a connectome? |
Srinivas Turaga
WHEN: Tuesday, June 21, 3 - 4 pm
WHERE: Lecture Hall, AI Research Building, Maria von Linden-Str. 6, 72076 Tübingen
HOST: Jakob Macke
ABSTRACT
We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. We show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements.
Paper: https://www.biorxiv.org/content/10.1101/2023.03.11.532232
Talk by Laura Filion - June 14, 2023
| Machine learning in soft matter |
Laura Filion
Physics Department, Soft Condensed Matter Group, Universiteit Utrecht
WHEN: Wednesday, 14.06.2023 at 2:15 pm
WHERE: N3, Hörsaalzentrum Morgenstelle, Universität Tübingen
HOST: Martin Oettel
Abstract
Developments in machine learning (ML) have opened the door to fully new methods for studying phase transitions due to their ability to extremely efficiently identify complex patterns in systems of many particles. Applications of machine learning techniques vary from the use of developing new ML-based methods for detecting complex crystal structures, to locating phase transitions, to speeding up simulations. The rapid emergence of multiple applications of machine learning to statistical mechanics and materials science demonstrates that these techniques are becoming an important tool for studying soft matter systems. In this talk, I will briefly present an overview of the work my group is doing on using machine learning to study soft matter systems with a focus on how machine learning might help us to understand the structure – or lack thereof – in glasses.
Guided tours around the Max Planck Campus - June 14, 2023
Guided tours around the Max Planck Campus
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June 14, 2023, 4:30pm - 6:00pm
Venue: Max-Planck-Campus, Tübingen
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Which institutions and facilities belong to the Cyber Valley ecosystem? What is their research focused on? The tour is part of the accompanying program of the exhibition "Cyber and the City".
The tour will be in German and therefore the event description is only available in German.
In den letzten Jahren entstanden viele neue Gebäude bei der Sternwarte in der Tübinger Nordstadt sowie auf dem Max-Planck-Campus. Welche Institutionen gehören zum Cyber Valley Ökosystem? Cyber Valley Eventmanagerin Tabea Brietzke erklärt in dieser kostenfreien Führung die Zusammenhänge und zeigt, wo an künstlicher Intelligenz (KI) geforscht wird. Die Führung macht Halt an verschiedenen Gebäuden, unter anderem dem Tübinger Max-Planck-Institut für intelligente Systeme. Sie endet am Tübingen AI Center, wo ein KI-Kunstprojekt zu erneuerbaren Energien aus dem Exzellenzcluster „Maschinelles Lernen. Neue Perspektiven für die Wissenschaft“ vorgestellt und ein Einblick in die Arbeit des Tübingen AI Centers gegeben wird.
Das Angebot ist Teil des Begleitprogramms der KI-Ausstellung „Cyber and the City – Künstliche Intelligenz bewegt Tübingen“ im Tübinger Stadtmuseum. Die Führung ist kostenfrei und richtet sich an alle Interessierten, Vorwissen zu KI ist nicht notwendig. Die Tour eignet sich auch für Rollstühle und Kinderwägen. Die Plätze sind auf 15 Personen begrenzt. Eine Anmeldung ist notwendig; der Anmeldelink wird zeitnah auf dieser Seite eingestellt. Die Details zum Treffpunkt bekommen Sie nach der Anmeldung per Mail zugeschickt.
Zur Anmeldung geht es hier.
Talk by Markus Ahlers - June 12, 2023
| The Ethical Significance of Pattern |
Markus Ahlers
Institute for Philosophy, Gottfried Wilhelm Leibniz University Hannover, Germany
WHEN: Monday, 12.06.2023 at 11:00
WHERE: Meeting Room ground floor, room 00-10/A12, AI Research Building,
Maria von Linden-Str. 6, 72076 Tübingen
Opening event of the "IN ML OUT" art exhibit, May 15 2023
Art exhibit „IN ML OUT“
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► Opening event on May 15, 2023, 6:00 pm
Venue: Staatliche Akademie der Bildenden Künste Stuttgart, Am Weißenhof 1, Neubau 2, 70191 Stuttgart
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Machine learning holds great potential for future renewable energy supply. In order to enter into an active dialog with citizens and decision makers, the exhibit "IN-ML-OUT" was initiated. It is the result of a cooperation of our researchers Nicole Ludwig and Nina Effenberger with design students of the "Staatliche Akademie der Bildenden Künste" in Stuttgart and the "Zentrum für rhetorische Wissenschaftskommunikationsforschung zur Künstlichen Intelligenz" (RHET AI) at the University of Tübingen.
"Explainability in Machine Learning", March 28 - 29, 2023
Workshop on "Explainability in Machine Learning"
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Tuesday March 28 and Wednesday March 29, 2023
Venue: Alte Aula, Münzgasse 30, 72070 Tübingen
Workshop organizers: Zeynep Akata, Stephan Alaniz, Christian Baumgartner, Almut Sophia Koepke, Massimiliano Mancini, Seong Joon Oh
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Deep learning has enabled major advances in machine learning. However, the deployment of deep learning frameworks in settings that are safety-critical or that impact society requires their decision-making to be explainable. This is fundamental for building trustworthy and user-oriented machine learning models. The aim of this workshop is to generate awareness around explainability in machine learning which is a topic of growing interest. Furthermore, we aim to encourage interdisciplinary interaction and collaboration between researchers from the University of Tübingen and other international institutions that work on different aspects of explainability, in particular in the context of computer vision.
Program: The workshop will contain both keynote talks from known researchers in the field as well as invited talks and spotlight presentations of recent advancements in the field of explainability.
► Preliminary program and more information: https://www.eml-unitue.de/eml-workshop
Spring School on Probabilistic Numerics, March 27 - 29, 2023
Spring School on Probabilistic Numerics
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Monday, March 27 - Wednesday, March 29, 2023
Venue: Audimax, Neue Aula, Geschwister-Scholl-Platz, 72074 Tübingen
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The first ever Probabilistic Numerics Spring School and Research Workshop consists of two days of (in-person) lectures, keynotes, and tutorial sessions, from the 27th to the 28th of March. It will be held in English, and is aimed at graduate students, researchers, and professionals interested in probabilistic numerical methods.The school features lectures and keynotes by leading experts, and hands-on code tutorials.
The school will be followed by a workshop on the 29th of March, 2023. The workshop offers a stage for advances in the probabilistic computation, by researchers working in the field.
For more information visit the Workshop webpage
Focus Session "Physics meets ML" at the DPG Spring Meeting, March 27, 2023
Spring Meeting of the German Physical Society (DPG), Section Condensed Matter Section
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Tuesday, March 27, 2023
Venue: Technische Universität Dresden - Campus Südvorstadt, Bergstraße 64, 01069 Dresden
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From 26 March to 31 March 2023 the DPG Spring Meeting of the Condensed Matter Section (SKM) will take place on the campus of the Technical University Dresden.
A Focus Session "Physics meets ML", organized by Sabine Andergassen and Moritz Helas, Martin Gärttner and Markus Schmitt, will be held on March 27, 2023. The corresponding tutorial will take place on March 26, 2023.
PROGRAM
Machine learning for complex quantum systems
Stefanie Czischek, University of Ottawa
Marcello Dalmonte, ICTP Trieste
Florian Marquardt, MPI & University of Erlangen-Nürnberg*
Christof Weitenberg, University of Hamburg
Understanding machine learning as complex interacting systems
Elena Agliari, Roma “La Sapienza”
Marc Mezard, Bocconi University
Manfred Opper, TU Berlin*
Cengiz Pehlevan, Harvard University
Zohar Ringel, Hebrew University of Jerusalem*
*Tutorial Lecturer
Workshop "Machine Learning MEETS Neurosciences", February 15, 2023
Workshop "Machine Learning meets Neurosciences"
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Wednesday, February 15, 2023 | 1:00 - 4:00 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen
Organizers: Tobias Kaufmann, Jakob Macke
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de).
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13:00 - 13:30 | Arrival, Lunch and Chat |
13:30 - 13:40 | Introduction Tobias Kaufmann (Medical Faculty, Computational Psychiatry) Jakob Macke (Computer Science Department, Machine Learning in Science) |
13:40 - 15:00 | Research Spotlights I (6 minutes presentation, 4 minutes discussion each) Peter Dayan (Max Planck Institute for Biological Cybernetics, Department Computational Neuroscience) Anna Levina (Computer Science Department, Self-organization of neuronal networks) Felix Wichmann (Computer Science Department, Neural Information Processing) Dmitry Kobak (Medical Faculty, Data Science for Vision Research) Charley Wu (Machine Learning Cluster of Excellence) Thomas Wolfers (Medical Faculty, Mental Health Mapping) Kathrin Brockmann (Medical Faculty, Neurology) |
15:00 - 15:20 | Coffee Break |
15:20 - 16:00 | Discussion: Which strategic topics should we be pursuing? |
16:00 - | Snacks and Refreshments |
Workshop "Machine Learning MEETS Quantitative Social Sciences", February 8, 2023
Workshop "Machine Learning meets Quantitative Social Sciences"
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Wednesday, February 8, 2023 | 08:25 - 04:00 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Dominik Papies, Augustin Kelava
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until February 3, 2023.
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08:00 | Arrival and Check-in with Coffee |
08:25 - 08:30 | Welcome Augustin Kelava (Methods Center), Dominik Papies (Department of Economics) |
08:30 - 08:45 | Estimating causal effects with double/debiased machine learning - a method evaluation Jonathan Fuhr (School of Business and Economics), Dominik Papies, Philipp Berens |
08:45 - 09:00 | Machine learning meets causal inference: An econometric perspective Michael Knaus (School of Business and Economics) |
09:00 - 09:15 | Random forests and double machine learning: Applications and extensions in labour economics Martin Biewen (School of Business and Economics), Philipp Kugler, Pascal Erhardt |
09:15 - 09:30 | The use of machine learning techniques in Psychometrics: A necessary transition for modern data designs Holger Brandt (Methods Center), Roberto Faleh, Patrick Schmidt, Zachary Roman |
09:30 - 09:45 | Optimized estimation of nonparametric causal mediation models via machine learning procedures Roberto Faleh (Methods Center), Holger Brandt |
09:45 - 10:00 | Discussion and Wrap-up 1 |
10:00 - 10:25 | Coffee Break |
10:25 - 10:40 | Generalized Mincer-Zarnowitz regressions Patrick Schmidt (Methods Center), Holger Brandt |
10:40 - 10:55 | Efficient estimation and forecasting of heterogeneous latent variable models for intensive longitudinal data Augustin Kelava (Methods Center) |
10:55 - 11:10 | Data-driven performance analysis utilizing machine learning techniques: identifying and improving players’ key performance factors in elite soccer Oliver Höner (Institute of Sports Science), Gabriel Anzer, Pascal Bauer, Benedikt Hosp, Augustin Kelava, Pascal Kilian, Daniel Leyhr, Florian Schultz |
11:10 - 11:25 | The Dirichlet-Horseshoe - A new shrinkage prior for both dense and sparse signal detection Lukas Fischer (Methods Center), Michael Nagel, Tim Pawlowski, Augustin Kelava |
11:25 - 11:40 | A deep learning model for complex interpretable and identified factor analysis – Evaluation on talent structure of youth soccer players Pascal Kilian (Methods Center), Daniel Leyhr, Oliver Höner, Augustin Kelava |
11:40 - 11:55 | Discussion and Wrap-up 2 |
11:55 - 12:40 | Lunch |
12:40 - 12:55 | Identifying distinct subgroups of suicidal ideations: A pre-registered ecological momentary assessment study in psychiatric patients Roman Zachary (Methods Center) |
12:55 - 13:10 | Explaining relationships between academic documents using generative transformer models Aseem Behl (School of Business and Economics) |
13:10 - 13:25 | The face of trustworthiness: Can a machine detect valid cues of trustworthy behavior in human faces? Stefan Mayer (School of Business and Economics), Agnes Bäker, Jan Landwehr, Martin Natter |
13:25 - 13:40 | Analysing structures of the scientific discourse with NLP tools Steffen Hillmert (Institute of Sociology) |
13:40 - 13:55 | Discussion and Wrap-up 3 |
13:55 - 14:15 | Coffee Break |
14:15 - 14:30 | Sports as a behavioral (economics) lab Tim Pawlowski (Institute of Sports Science) |
14:30 - 14:45 | Machine learning methods for large survey data in social sciences: Challenges, solutions, and future directions Kou Murayama (Hector Research Institute of Education Sciences and Psychology), Rosa Lavelle-Hill |
14:45 - 15:00 | Machine learning for modelling, assessing and supporting educational processes Thorsten Bohl (Tübingen School of Education), Xiaobin Chen, Ulrike Cress, Peter Gerjets, Andreas Lachner, Detmar Meurers, Kou Murayama, Ulrich Trautwein |
15:00 - 15:15 | Quantification of stock risk premia using theory-based/option-implied methods and machine learning approaches, and testing conditional factor asset pricing models with the help of ML-methods Steffen Hillmert Joachim Grammig (School of Business and Economics), Constantin Hanenberg, Christian Schlag, Jantje Sönksen |
15:15 - 15:30 | Combining ML and psychometrics is a two-way street – Example for mixed effects in machine learning Pascal Kilian (Methods Center), Sangbaek Ye, Augustin Kelava |
15:30 - 15:45 | Discussion and Wrap-up 4 |
15:45 - 16:00 | What follows next |
16:00 - | Snacks and Refreshments |
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Workshop "Machine Learning MEETS Machine Learning“, February 1, 2023
Workshop "Machine Learning meets Machine Learning“
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Wednesday, February 1, 2023 | 11:00 am - 02:00 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Philipp Hennig, Matthias Hein
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until January 27, 2023.
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11:00 - 11:15 | Arrival and Welcome |
11:15 - 11:30 | Pitch I: Mnemosyne — End-to-End Trustworthy Data Analytics Bob Williamson (Department of Computer Science, University of Tübingen) |
11:30 - 11:45 | Pitch II: Beyond High Performance and Big Data - New Paradigms for Scientific Computing Philipp Hennig (Department of Computer Science, University of Tübingen) |
11:45 - 12:00 | Pitch III: NLP in Medicine Carsten Eickhoff (Medical Faculty, University of Tübingen) |
12:00 - 12:15 | Discussion: Spontaneous Additional Proposals |
12:15 - 13:00 | Lunch + Strategic Structure Discussion: Which topics are wemissing/not covering well? |
13:00 - 13:15 | Consolidation, forming groups |
13:15 - 14:00 | Small Groups write short (0.5 page) summaries, strategic points |
14:00 - | Snacks and Refreshments |
Workshop "Machine Learning MEETS Physics", February 1, 2023
Workshop "Machine Learning meets Physics"
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Wednesday, February 01, 2023 | 01:00 - 05:00 pm
Venue: Auf der Morgenstelle 10, Building C, Room 7E02
Organizers: Martin Oettel, Frank Schreiber
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).
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13:00 - 13:15 | Arrival and Welcome |
13:15 - 13:20 | Introduction Martin Oettel (Department of Physics, University of Tübingen) |
13:20 - 14:15 | Spotlight Presentations:
Learning Generators of (open) System Quantum Dynamics
Finding Classical Density Functionals and Power Functionals in Analytic Form
ML on Scattering Data (X-Rays and Neutrons)
A few observations on where ML is really good at and where it can make a difference. Mostly to spark thoughts.
Neuromorphic Computing with Strongly Correlated Materials
ML in Quantum Metrology |
14:15 - 15:00 | Discussion and Coffee Break |
15:00 - 15:15 | Mechanistic Models and Machine Learning Jakob Macke (Department of Computer Science, University of Tübingen) |
15:15 - 17:00 | Discussion about Topics of mutual benefit, Potential "hot" topics, Method development vs. application of methods, etc. |
17:00 - | Open End |
Workshop "Machine Learning MEETS Qualitative Social Sciences", January 25, 2023
Workshop "Machine Learning meets Qualitative Social Sciences"
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Wednesday, January 25, 2023 | 09:00 - 1:30 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Christoph Bareither, Thomas Thiemeyer
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until January 20, 2023.
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Panel 1: Human-ML-Relationships in Scientific Practice
09:00 - 09:30 | Human-ML-Relationships from the Perspective of Digital Anthropology Christoph Bareither (Institute of Historical and Cultural Anthropology, University of Tübingen) |
09:30 - 10:00 | Data Robert C. Williamson (Department of Computer Science, University of Tübingen) |
10:00 - 10:30 | Improving Teamwork between Humans and ML Samira Samadi (Max Planck Institute for Intelligent Systems, Tübingen) |
10:30 - 11:00 | ML in (Scientific) Contexts: Ethical, Societal, and Philosophical Questions Regina Ammicht Quinn, Jessica Heesen, Wulf Loh (International Center for Ethics in the Sciences and Humanities (IZEW), University of Tübingen) |
11:00 - 11:30 | Coffee Break |
Panel 2: Machine Learning and Society
11:30 - 12:00 | Morals and Methodology Konstantin Genin (Cluster of Excellence Machine Learning for Science, University of Tübingen) |
12:00 - 12:30 | The Social Foundations of Computation Moritz Hardt (Max Planck Institute for Intelligent Systems, Tübingen) |
12:30 - 13:00 | ML in Tübingen Ulrike von Luxburg (Department of Computer Science, University of Tübingen) Thomas Thiemeyer (Institute of Historical and Cultural Anthropology, University of Tübingen) |
13:00 - 13:30 | General Discussion & Concluding Remarks |
13:30 - | Lunch organized by the Excellence Cluster ML for Science |
Workshop "Machine Learning MEETS Geosciences", January 25, 2023
Workshop "Machine Learning meets Geosciences"
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Wednesday, January 25, 2023 | 02:00 - 5:00 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Thomas Scholten, Todd Ehlers
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until 20. Januar 2023
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13:30 - 14:00 | Arrival and Lunch |
Panel 1: Current Research Topics in Geosciences
14:00 - 14:15 | Causal Explanations for Model Interpretation in Digital Soil Mapping Nafiseh Kakhani (Department of Geosciences, University of Tübingen) |
14:15 - 14:30 | Using ML for Inversion of Mechanistic Forward Models in Geophysics Reinhard Drews (Department of Geosciences, University of Tübingen) |
14:30 - 14:45 | ISOCLIM: Exploring Isotopic Constraints on Future Climate Variability Kira Rehfeld (Department of Geosciences, University of Tübingen) |
14:45 - 15:00 | Discussion: Additional Statements from Geosciences |
15:00 - 15:15 | Coffee Break |
Panel 2: Current Research Topics in Machine Learning
15:15 - 15:30 | Inference with Computational Uncertainty Philipp Hennig (Department of Computer Science, University of Tübingen) |
15:30 - 15:45 | Machine Learning in Science Jakob Macke (Department of Computer Science, University of Tübingen) |
15:45 - 16:00 | Modeling SpatioTemporal Dynamics Martin Butz (Department of Computer Science, University of Tübingen) |
16:00 - 16:15 | Discussion: Additional Statements from ML |
16:15 - 17:00 | General Discussion & Concluding Remarks |
17:00 - | Snacks and Refreshments |
Workshop "Machine Learning MEETS Medicine", January 18, 2023
Workshop "Machine Learning meets Medicine"
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Wednesday, January 18, 2023 | 1:00 - 4:00
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Manfred Claassen, Sergios Gatidis
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)
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12:30 – 13:00 | Arrival and Lunch |
13:00 – 14:30 | Impulse Pitches Josef Leibold (Medical Faculty, Functional Immunogenomics) Bettina Weigelin (Werner Siemens Imaging Center, Preclinical Imaging of the Immune System) Michael Bitzer / Pavlos Missios (Medical Faculty, Gastroenterology) Daniela Thorwarth (Medical Faculty, Biomedical Physics) Tobias Kaufmann (Medical Faculty, Computational Psychiatry) Christian Baumgartner (Machine Learning Cluster of Excellence, Machine Learning in Medical Image Analysis) Carsten Eickhoff (Medical Faculty, Medical Informatics) |
14:30 – 16:00 | Discussion about Strategic Directions |
16:00 – | Snacks and Refreshments |
Workshop "Machine Learning MEETS Linguistics", January 11, 2023
Workshop "Machine Learning meets Linguistics"
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Wednesday, January 11, 2023 | 2:00 - 5:00 pm
Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6
Organizers: Michael Franke, Detmar Meurers
Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)
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13:30 – 14:00 | Arrival and Lunch |
14:00 – 14:10 | Workshop Goals: Reiterate Mission Statement |
14:10 – 14:25 | Explainability in Deep Learning through Communication Zeynep Akaty (Department of Computer Science, University of Tübingen) |
14:25 – 14:40 | Current Collaborative Projects with ML Involving the Quantitative Linguistics Group Harald Baayen (Department of Linguistics, University of Tübingen) |
14:40 – 14:55 | Event-Predictive Language Grounding Martin Butz (Department of Computer Science, University of Tübingen) |
14:55 – 15:10 | Probabilistic Pragmatics Meets (Needs?) ML Michael Franke (Department of Linguistics, University of Tübingen) |
15:10 – 15:30 | Coffee Break |
15:30 – 15:45 | Generative Models of Language Change Gerhard Jäger (Department of Linguistics, University of Tübingen) |
15:45 – 16:00 | Title to be defined Hendrik Lensch (Department of Computer Science, University of Tübingen)
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16:00 – 16:15 | Title to be defined Detmar Meurers (Department of Linguistics, University of Tübingen) |
16:15 – 16:30 | Using Cognitive Psychology to Understand GPT-3 Eric Schulz (Max Planck Institute for Biological Cybernetics) |
16:30 – 17:00 | General Discussion & Concluding Remarks |
17:00 – | Snacks and Refreshments |
2022
Philosophy of Science Meets Machine Learning (PhilML), October 20 - 22, 2022
Philosophy of Science Meets Machine Learning (PhilML)
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October 20 - 22, 2022
Lecture Hall, Max-Planck-Guest House, Max-Planck-Ring 6, 72076 Tübingen
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More information about the event as well as the program can be found here.
4th Annual Conference "Machine Learning in Science", July 12 + 13, 2022
4th Cluster Conference
"Machine Learning in Science" 2022
► Tuesday, July 12 | 9:00 am - 6:00 pm |
► Wednesday, July 13 | 9:00 am - 5:00 pm |
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Venue: Westspitze, Eisenbahnstraße 1, 72072 Tübingen
For all those who cannot attend the conference in person, we will stream the presentations on
Zoom
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PROGRAM
All times are provided in CEST, Central European Summer Time.
Tuesday, July 12, 2022
09:00 | Ulrike von Luxburg, Philipp Berens ► Opening Remarks |
09:15 | Gerard Pons-Moll ► Virtual Humans - From Appearance to Behaviour |
10:00 | Kira Rehfeld ► Understanding Past, Present and Future Climate Evolution: Between Facts, Physics and Fiction |
10:45 | Coffee Break |
11:15 | Setareh Maghsudi ► Linear Combinatorial Semi-Bandit with Causally Related Rewards |
12:00 | Flora Jay ► KEYNOTE: Digging Historical Diversity Patterns out of Large-Scale Genomic Data using Exchangeable and Generative Neural Networks talk online only |
13:00 | Lunch Break |
14:15 | Michèle Finck ► The EU's Legislative Agenda on AI |
15:00 | Frank Schreiber ► Machine Learning Applied to Scattering |
15:45 | Celestine Mendler-Dünner ► Social Dynamics in Learning and Decision-Making |
16:30 | Poster Session and Coffee |
19:00 | Conference Dinner at "Freistil" (registration required) |
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Wednesday, July 13, 2022
09:00 | Sergios Gatidis ► Machine Learning for Science - and What About the Real World? Challenges for ML in Medicine |
09:45 | Michael Franke ► Probabilistic Models of Language Use |
10:30 | Charley Wu ► The Trajectory of Human Development Resembles Stochastic Optimization in the Space of Learning Strategies |
11:00 | Coffee Break |
11:15 | Devis Tuia ► KEYNOTE: Machine Learning Supporting Ecology Supporting Machine Learning talk online only |
12:15 | Richard Gao ► Simulation-based inference for discovering mechanistic models of neural population dynamics |
12:45 | Almut Sophia Köpke ► Multi-Modal Learning with Visual Information, Language, and Sound |
13:15 | Lunch Break |
14:15 | Cluster Network Project |
14:45 | Cluster Network Project |
15:15 | Cluster Network Project |
15:45 | Cluster Network Project |
16:15 | Cluster Network Project |
16:45 | Ulrike von Luxburg, Philipp Berens ► Closing Remarks |
Science and Innovation Days, June 29 - July 2, 2022
Science and Innovation Days
From June 29 to July 2, 2022, Tübingen's largest research institutions will open their doors and present their research to interested public. The Science and Innovation Days will take place all over the city, the program can be found here. The events are in German.
The members of our cluster participate with the following contributions:
Wednesday, June 29, 2022
18:15 | With Eric Schulz ► Kick Off: Wissenschafft Zukunft - Wissenschaft und Gesellschaft im Dialog |
20:30 | With Nicole Ludwig ► Künstliche Intelligenzen der Zukunft: Fakten und Fiktionen (Lesung) More Info: https://uni-tuebingen.de/de/230849#c1565249 |
Friday, July 1, 2022
20:00 | With Robert Bamler ► Wieviel Science steckt in der Fiction? - Künstliche Intelligenz in Film und Forschung (Film und Diskussion) More Info: https://uni-tuebingen.de/de/230858#c1583855 |
Saturday, July 2, 2022
09:30 - 10:30 | With Tilman Gocht ► Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort More Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 12:00 | With Kerstin Rau, Thomas Gläßle ► Wie gut versteht eine Maschine die Natur? Vorhersage von Bodentypen im Schönbuch More Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 12:00 | With Georg Martius, Huanbo Sun ► Wie Roboter fühlen können - ein sensitiver Roboterfinger mit Tastsinn More Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 10:30 and 11:30 - 12:00 | For Kids ► Bernhard Schölkopf: Warum sind Computer dumm? More Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 13:00 | With Philipp Hennig ► Info- und Feedbackstand: Was macht Cyber Valley? More Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:30 - 11:15 | With Tilman Gocht ► Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort More Info: https://uni-tuebingen.de/de/230852#c1583873 |
13:15 | For Kids ► Andreas Geiger: Kann künstliche Intelligenz kreativ sein? ( bereits ausgebucht) Workshop for children as part of the Children's University Research Day More Info: https://uni-tuebingen.de/de/2626#c547865 |
15:15 | For Kids ► Andreas Geiger: Kann künstliche Intelligenz kreativ sein? Workshop for children as part of the Children's University Research Day. Registration until June 29 at kinderunispam prevention@uni-tuebingen.de More Info: https://uni-tuebingen.de/de/2626#c548369 |
Workshop „AI and ML Research and Democracy”, April 2 - 4, 2022
PhD Workshop "Artificial Intelligence and Machine Learning Research and Democracy" followed by public panel discussion
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April 2 - 4, 2022, at the University of Tübingen
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About the workshop:
As the use of Machine Learning algorithms and Artificial Intelligence pervades all areas of our lives and societies, it also affects fundamental pillars of democracy, such as public debate, political decision making and (dis)information through media. In our PhD workshop “Artificial Intelligence and Machine Learning research and democracy – an interdisciplinary perspective” we will provide a platform for young researchers from all relevant disciplines – from machine learning research to political science and every nuance in between – to engage in exchange of methods and current state of the art research. Our guiding question will be: How can we shape the co-development of AI/ ML research and democracy?
More information: ai-and-democracy-workshop.de
„KI gestaltet Demokratie – Demokratie gestaltet KI“ on April 4, starting 6:30 pm
At the end of the workshop, a public discussion on "AI and Democracy" will take place on April 4, starting 6:30 p.m., at the "Westspitze" in Tübingen.
Everyone interested is cordially invited to this event.
Registration via the following link: ai-and-democracy-workshop.de/podium
Workshop "Introduction to Machine Learning", March 30, 2022
Workshop on Introduction to Machine Learning
The Machine Learning ⇌ Science Colaboratory is glad to invite you to its in-person, hands-on Workshop on Introduction to Machine Learning.
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Wednesday, March 30, 2022, 9:00 to 13:00
The event will take place on site at Maria-von-Linden-str. 6, 72076 Tübingen, Lecture Hall (ground floor, room 00-28/A7).
Registration:
Apply for the workshop by filling out the Registration Form
Please note that attendance is limited to 12 participants! You will hear from us approximately one week before the event.
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What? We’ll present first the fundamentals of ML with the aid of interactive demonstrations. Equipped with the necessary concepts, we will then jointly look at your specific research problems and data, and discuss how to design a ML project for them.
Who? Researchers from MSc student to PI level at the University of Tübingen who would like to know more about machine learning and how it can help with their research. Scholars from all disciplines are invited to join, no math or programming skills will be assumed.
How to prepare? Think about the characteristics of relevant datasets that you have access to, and be ready to discuss your use-case with the group. Bring your own laptop.
Covid regulations: 3G-rules apply (proof of vaccination or past infections or negative test) + FFP2 mask
For more information, please visit the Machine Learning ⇌ Science Colaboratory Website
2021
Workshop "Philosophy of Science Meets Machine Learning", November 9 - 12, 2021
Workshop "Philosophy of Science Meets Machine Learning"
The Workshop takes place in person in Tübingen
on Nov. 9 - 10 in the Alte Aula (Münzgasse 30)
on Nov. 11 - 12 in the Max Planck Institute for Intelligent Systems (Max-Planck-Ring 4)
The workshop is organised by the ‘Ethics and Philosophy Lab’ of the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen (Workshop Convenors: Thomas Grote, Thilo Hagendorff, Eric Raidl).
Registration: Space is limited. Guests please register via: rebigtimspam prevention@googlemail.com
Machine learning does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science, epistemology, research ethics and cognate fields such as sociology of science. The workshop will bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. The workshop`s central topics are:
- A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
- The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
- The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
- Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.
Program
DAY 1: November 9 - Alte Aula
13:00 | Registration & Coffee |
13:50 | Short Introduction |
14:00 - 14:50 | Gregory Wheeler - "Discounting Desirable Gambles" |
15:00 - 15:40 | Vlasta Sikimic - "Algorithmic grant review: benefits and limitations" |
15:50 - 16:40 | Emily Sullivan - "Stopping the Opacity Regress" |
16:40 - 17:00 | Coffee & Snacks |
17:00 - 17:50 | Bob Williamson - "(Un)stable facts, and (missing) chains of reference in machine learning" |
Evening activities / Dinner |
DAY 2: November 10 - Alte Aula
9:00 - 9:50 | Carlos Zednik - "The Explanatory Role of Explainable Artificial Intelligence" |
10:00 - 10:40 | Moritz Renftle et al. - "Evaluating the Effect of XAI on the Understanding of Machine Learning Models" |
10:40 - 11:20 | Timo Freiesleben - "To Explain and to Predict - Explanatory Machine Learning Models in Science" |
11:20 - 11:40 | Coffee & Snacks |
11:40 - 12:30 | Alex Broadbent - "Predictive Investigation and Deep Learning" |
12:30 - 14:00 | Extended lunch break |
14:00 - 14:50 | Jon Williamson - "Evidential Pluralism and Explainable AI" |
15:00 - 15:40 | Oliver Buchholz - "Towards a Means-End Account of XAI" |
15:40 - 16:00 | Break |
16:00 - 16:40 | Koray Karaca - "Inductive Risk and Values in Machine Learning" |
16:40 - 17:30 | Lena Kästner - "Grasping Psychopathology: On Complex and Computational Models" |
Informal discussion / Dinner |
DAY 3: November 11 - MPI-IS
9:30 - 10:10 | Benedikt Hoeltgen - "Causal Variable Selectrion Through Neural Networks" |
10:10 - 10:50 | Daniela Schuster - "Suspension of Judgment and Explainable AI" |
10:50 - 11: 20 | Coffee & Snacks |
11:20 - 12:10 | Anouk Barberousse - "Can Concept of Scientific Knowledge be Transformed by Machine Learning?" |
12:10 - 14:00 | Extended lunch break |
14:00 - 14:40 | Giorgio Gnecco et al. - "Simple Models in Complex Worlds: Occam's Razor and Statistcal Learning Theory" (Online) |
14:40 - 15:20 | Atoosa Kasirzadeh - "Kinds of Explanation in Machine Learning" (Online) |
15:20 - 15:50 | Coffee Break |
15:50 - 16:30 | Tim Räz - "Understanding Machine Learning for Empiricists" |
16:30 - 17:20 | Carina Prunkl - "Predictive Investigation and Deep Learning" |
Informal Discussion / Dinner |
DAY 4: November 12 - MPI-IS
9:30 - 10:10 | Mario Günther - "How to Attribute Beliefs to AI Systems?" (Online) |
10:10 - 10:50 | Dilectiss Liu - "Epistemic Opacity Does Not Undermine the Epistemic Justification of Machine Learning Models" |
11:00 - 11:50 | Kate Vredenburgh - "Against Rational Explanations" |
11:50 - 12:20 | Coffee Break |
12:20 - 13:00 | Roundtable |
Talk by Claire Vernade - September 30, 2021
| Bandit learning with delays in Non-stationary environments |
Claire Vernade
WHEN: Thursday, 30. September 2021, 9:00 am
WHERE: Hybrid-Event:
Claire will give her talk in the Lecture Hall, AI Building, Maria-von-Linden-Straße 6. Due to the current Covid restrictions, seats are limited. If you are a PI of our Cluster and want to join in person, please contact Elena Sizana to register. You need to be fully vaccinated, tested or recovered.
We invite everyone else to participate via Zoom: https://zoom.us/j/99906352769
ABSTRACT
We consider the problem of learning with delayed bandit feedback, meaning by trial and error, in changing environments. This problem is ubiquitous in many online recommender systems that aim at showing content, which is ultimately evaluated by long-term metrics like a purchase, or a watching time. Mitigating the effects of delays in stationary environments is well-understood, but the problem becomes much more challenging when the environment changes. In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete. However, the arising issues can be addressed if relevant intermediate signals are available without delay, such that given those signals, the long-term behavior of the system is stationary. To model this situation, we introduce the problem of stochastic, non-stationary and delayed bandits with intermediate observations. We develop a computationally efficient algorithm based on UCRL, and prove sublinear regret guarantees for its performance.
Workshop "Simulation-based Inference for scientific discovery", 2021 September 20-22, ONLINE
Workshop "Simulation-based Inference for scientific discovery"
This workshop will be taking place online on Zoom.
The workshop is jointly organized by the ML⇌Science Colaboratory, the MLS Chair (Jakob Macke) and Helmholtz AI.
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Registration:
Apply for the workshop by filling out the Registration Form Deadlin for application is Aug 31, 2021.
We can only select 20 participants for the workshop. To come up with that list and make a final decision, we will form a small selection board. We will inform successful applicants subsequently.
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You use simulation in physics, economics, archaeology or any other domain of your choice? You want to find the simulator's parameters that best fits the observations? Then simulation-based inference is something for you!
We teach a practical simulation-based inference workshop to help you understand recent machine learning techniques and apply them to your problem.
Apply to learn, have fun, and participate in a supportive and inclusive community. The workshop will combine lectures and practical hands-on sessions by experts in the field. We strive to provide a seamless computing environment for you to focus on the content rather than in import errors.
We plan to cover the following topics:
| Program
20. September | Using simulators for discovery, introduction to conditional density estimation.
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21. September | How does neural simulation-based inference work?
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22. September | Applying the sbi toolbox to your problem. Pitfalls, tricks and opportunities!
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3rd Annual Conference "Machine Learning in Science", July 12 + 13, 2021
3rd Cluster Conference
"Machine Learning in Science" 2021
► Monday, July 12 | 2:00 pm - 6:00 pm | Registration Link
followed by an online theater at 19:30
► Tuesday, July 13 | 2:00 pm - 6:00 pm | Registration Link
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The conference will take place virtually on Zoom.
The event is open to the public. Registration is required, separate for each day, see Registration Links above.
The theater on Monday evening is open to the public, further information here. The play will be performed in English and will be streamed live on Youtube. Afterwards, there will be a discussion with the actors and some researchers from our cluster. Registration is not required.
Links:
For the play https://tinyurl.com/SiliconWoman
For the discussion afterwards on Zoom https://zoom.us/j/91670801978
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PROGRAM
All times are provided in CEST, Central European Summer Time.
Monday, July 12, 2021
14:00 - 14:15 | Ulrike von Luxburg, Philipp Berens ► Opening remarks |
14:15 - 14:45 | Robert Bamler ► Maintaining Individual Agency in the Age of Big Data: Baby Steps |
14:45 - 15:15 | Caterina De Bacco ► Learning Reciprocity and Community Patterns in Networks |
15:15 -15:30 | BREAK |
15:30 - 16:00 | Konstantin Genin ► Clinical Equipoise and Causal Discovery |
16:00 - 16:45 | Spotlight Presentations Innovation Fund Projects of the Cluster of Excellence "Machine Learning" |
| 16:00 - 16:10 David Künstle ► Machine Learning Approaches for Psychophysics with Ordinal Comparisons |
| 16:10 - 16:20 Thomas Gläßle / Kerstin Rau ► Interpretable Spatial Machine Learning for Environmental Modelling |
| 16:20 - 16:30 Daniel Weber ► Human-Robot Interface with Eye-Tracking |
| 16:30 - 16:40 Valentyn Boreiko ► Counterfactual Explanations of Decisions of Deep Neural Networks with Applications in Medical Diagnostics |
16:45 - 17:00 | BREAK |
17:00 - 17:30 | Spotlight Presentations Innovation Fund Projects of the Cluster of Excellence "Machine Learning" |
17:00 - 17:10 Susanne Zabel ► Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data | |
17:10 - 17:20 Lukas Fischer / Michael Nagel ► Modelling Behavioral Responses to Emotional Cues in Sports - A Bayesian Approach | |
17:20 - 17:30 Francesco Carnazza ► Understanding Quantum Effects in Neural Network Models through ML | |
17:30 - 18:00 | Manfred Claassen ► (Weakly) Supervised Learning of Disease Associated Cell States and Dynamics |
18:00 - 19:30 | BREAK |
19:30 - 20:15 | Theater ► Silicon Woman - the Singing Cyborg |
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Tuesday, July 13, 2021
14:00 - 14:50 | Keynote Lecture Neil Lawrence (The DeepMind Professor of Machine Learning, University of Cambridge) ► Machine Learning and the Physical World |
14:50 - 15:20 | Samira Samadi ► Socially Fair k-Means Clustering |
15:20 - 15:30 | BREAK |
15:30 - 16:30 | Spotlight Presentations |
| 15:30 - 15:40 Matthias Karlbauer ► Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent CNN |
| 15:40 - 15:50 Pablo Sanchez Martin ► Extracting Expertise from Tweets: Exploring the Boundary Conditions of Ambient Awareness |
| 15:50 - 16:00 Zohreh Ghaderi / Hassan Shahmohammadi ► Enhancing Machine Learning of Lexical Semantics with Image Mining |
| 16:00 - 16:10 Jonathan Fuhr ► Applied Casual Inference in Social Sciences and Medicine |
| 16:10 - 16:20 Jonas Ditz ► Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response |
| 16:20 - 17:30 Alessandro Simon ► Analytic Classical Density Functionals from an Equation Learning Network |
16:30 - 16:45 | BREAK |
16:45 - 17:15 | Peter Ochs ► Optimization for Machine Learning |
17:15 - 17:45 | Enkelejda Kasneci ► Machine Learning for intelligent Human-Computer Interaction |
17:45 - 18:00 | Ulrike von Luxburg / Philipp Berens ► Closing Remarks |
2020
Workshop on the "Philosophy of Medical AI", October 08-09, 2020 -- ONLINE
Virtual Workshop on the Philosophy of Medical AI
► Thursday, October 08 | 09:30 - 17:30
► Friday, October 09 | 10:00 - 16:00
Registration
The workshop is open to the public, no registration will be needed.
Convenor
Thomas Grote; Ethics and Philosophy Lab; Cluster of Excellence “Machine Learning: New Perspectives for Science”
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The workshop will take place virtually on Zoom.
Link to Meeting on THURSDAY Meeting-ID: 977 8903 0792
Link to Meeting on FRIDAY Meeting-ID: 990 9618 3434
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Recent advances in deep learning have fuelled the interest in applying AI-systems within healthcare. Indeed, a vast literature of high-profile studies indicates, that the opportunities that AI provides for different branches of medicine are manifold: From improving medical diagnosis, to the timely prediction of health-risks and the discovery of new drugs. At the same time, there are worries that the imperfections of current AI systems might perpetuate systemic ills in the healthcare system or even create new ethical problems. The aim of this workshop is to reflect on the opportunities and challenges of utilising AI in medicine. To this end, the workshop brings together philosophers of science, medical ethicists as well as researchers in machine learning or bioinformatics.
Thursday October 08, 2020
09:30 - 10:00 | Welcome address and brief introduction |
10:00 - 10:50 | Sune Holm (University of Copenhagen) Equality and Fair Algorithmic Decision Making |
11:00 - 11:50 | Atoosa Kasirzadeh (Australian National University/University of Toronto) The Use and Misuse of Counterfactuals in Fair Machine Learning |
| LUNCH BREAK |
13:00 - 13:50 | Georg Starke (University of Basel) Does Trust Constitute an Adequate Epistemic Stance Towards Medical AI? |
14:00 - 14:50 | Geoff Keeling (Stanford University) Decision-Support Systems and Clinical Reasoning – A Peircian Approach |
| BREAK |
15:30 -16:20 | Manfred Claassen (University of Tübingen) Challenges in machine learning driven translation of single-cell biology studies |
16:30 - 17:30 | Alex London (Carnegie Mellon University, Pittsburgh) Keynote: Ethics in Medical AI: Explaining Models vs Explaining the Warrant for Their Use |
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Friday October 09, 2020
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11:00 - 11:50 | Chris Burr (Alan Turing Institute) Responsible Innovation and Digital Psychiatry |
| LUNCH BREAK |
13:00 - 13:50 | Rune Nyrup (University of Cambridge) Value Transparency in Science and Machine Learning |
14:00 - 14:50 | Zeynep Akata (University of Tübingen) Explaining Neural Network Decisions Via Natural Language |
15:00 - 16:00 | Alex Broadbent (University of Johannesburg) Keynote: Why Robots Cannot Do Epidemiology |
Machine Learning meets Environmental Science, September 25, 2020
Machine Learning meets Environmental Science
Friday, September 25, 2020
Meeting Venue
Neue Aula, Audimax, Geschwister-Scholl-Platz
Organizers
Prof. Martin Butz (ML Cluster, Dep. Of Computer Science)
Prof. Christiane Zarfl (Center for Applied Geoscience)
Registration
Registration is required by Email until Sept 22, 2020.
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PROGRAM
09:00 Welcome and Introduction: Cluster of Excellence – Machine Learning for Science
Philipp Berens
09:10 ML Transfer Center and simulation based inference
Álvaro Tejero-Cantero & Jakob Macke
09:40 Day-ahead optimization of production schedules for saving electrical energy costs
Thomas Stüber & Michael Menth
10:00 Uncovering hidden structure in climate data
Bedartha Goswami
10:20 Coffee Break
10:40 Learning spatiotemporal distributed generative graph neural networks
Martin Butz
11:00 Modeling environmental processes in rivers
Christiane Zarfl
11:20 Can plants learn? Coupling models and data in eco-evolutionary research
Sara Tomiolo & Maximiliane Herberich (from Katja Tielbörger’s group)
11:40 Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by
stacking machine learning models and rescanning covariate space
Ruhollah Taghizadeh-Mehrjardi (from Thomas Scholten’s group)
12:00 The climate situation: Facts and Necessities
Harald Baayen
12:05 Snack Break
12:30 ML and mobile robots in environmental science
Andreas Zell
12:50 Turbulent transport of energy, momentum and matter by large data sets
obtained from airborne probing of the lower atmosphere
Jens Bange
13:20 Status of the CRC 1253 CAMPOS - Catchments as Reactors:
Metabolism of Pollutants on the Landscape Scale
Christiane Zarfl
13:40 Funding options in the ML Cluster of Excellence and beyond
Tilman Gocht
14:00 End
_______________________________________________________________________________________________________________________________________________________________________________________________
Aim of the day
Environmental science studies spatio-temporal dynamics of various processes and on different topics, including climate and weather, geology, hydrology, vegetation and agriculture, various forms of pollution (e.g. of organic pollutants), to name just a few. In all these cases, multiple, often interrelated data sources are available at varying degrees of spatial and temporal granularity. Moreover, human activities, such as river dam building, CO2 release, plantations, industry etc., strongly influence the unfolding dynamics. Critical principles – such as basic laws in physics – apply universally in such systems. Environmental science has strong expertise in modeling the underlying processes – typically by systems of partial or ordinary differential equations.
As a result, besides the expertise about the underlying processes, environmental science offers two types of data – real-world data as well as data from the respectively available models of the considered environmental system. This offers essentially the perfect basis for a meaningful, science-driven application of ML algorithms. On the one hand, the parameters of the differential equations may be optimized more effectively by means of state-of-the-art gradient-based approximation approaches from ML. On the other hand, the available models may be augmented or fully substituted by distributed spatio-temporal, generative neural network approaches, such as convolutional networks, graph networks, autoencoders, recurrent neural networks, and combinations thereof.
Seeing that models are available to pre-train and analyze potentially applicable ML architectures, expertise is available to tune these models to the actual underlying processes, and that real world data is available to further train and test the generalizability of these ML architectures, it is time that ML meets Environmental Science! The aim is to foster collaboration with a focus on two main potential strands. First, available models of differential equations and involved prior situation assumptions may be optimized by means of state-of-the-art ML techniques. Second, ML techniques and particularly distributed, generative artificial neural networks may be designed to infer the processes and structures that generate particular data patterns, thus enabling (i) the fast, efficient, and accurate simulation of environmental processes and (ii) the consideration of impacts of human actions, including the potential to derive optimal actions for steering the environmental system towards a desired (stable / homeostatic) direction.
2nd Annual Conference "Machine Learning in Science", July 21-23, 2020 -- ONLINE
2nd Cluster Conference
"Machine Learning in Science" 2020
► Tuesday, July 21 | 2:00 pm - 5:45 pm
► Wednesday, July 22 | 2:00 pm - 6:15 pm
► Thursday, July 23 | 2:00 pm - 6:30 pm
Change in program, Thursday July 23: Manfred Claassen's talk at 2 pm unfortunately needs to be cancelled. Phillip Berens kindly takes over the time slot at short notice with a talk on "Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance" contenteditable="false".
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The conference will take place virtually on Crowdcast with a live stream on Youtube.
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Registration
The conference is open to the public.
To participate in the discussions via chat you will have to register via Crowdcast for each conference day by clicking on the corresponding event on our Crowdcast profile - the participants' cameras and microphones will remain switched off. You need to enter your email address first and then your full name (first name and surname).
If you only want to follow the talks, you can watch them via Youtube. Here you find the live streams of all conference talks.
Each talk takes 30 minutes plus 15 minutes discussion, each spotlight presentation 5 minutes plus 5 minutes discussion.
Important: All times are given in CEST, Central European Summer Time.
Tuesday, July 21, 2020
14:00 - 14:15 | Ulrike von Luxburg, Philipp Berens ► Opening Remarks ► Welcome Address |
14:15 - 15:00 | Kyle Cranmer (Center for Cosmology and Particle Physics, New York University) ► Keynote Lecture: How Machine Learning Can Help us Get the Most out of our Highest Fidelity Physical Models |
15:00 - 15:45 | Zeynep Akata (Department of Computer Science and Cluster of Excellence “Machine Learning”, University of Tübingen) ► Learning Decision Trees Recurrently through Communication |
15:45 - 16:00 | BREAK |
| Spotlight Presentations |
16:00 - 16:10 | David Künstle ► Machine Learning Approaches for Psychophysics with Ordinal Comparisons |
16:10 - 16:20 | Zohreh Ghaderi / Hassan Shahmohammadi ► Enhancing Machine Learning of Lexical Semantics with Image Mining |
16:20 - 16:30 | Matthias Karlbauer ► Causal Inference with a Spatio-Temporal Generative Model |
16:30 - 16:40 | Thomas Gläßle / Kerstin Rau ► Interpretable Spatial Machine Learning for Environmental Modelling |
16:40 - 17:00 | BREAK |
17:00 - 17:45 | Jakob Macke (Department of Computer Science and Cluster of Excellence “Machine Learning”, University of Tübingen ► Training Neural Networks to Identify Mechanistic Models of Neural Networks |
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Wednesday, July 22, 2020
14:00 - 14:45 | Peter Dayan (Max Planck Institute for Biological Cybernetics, Tübingen) ► Modelling and Manipulating Behaviour Using Recurrent Networks |
14:45 - 15:30 | Dominik Papies (Faculty of Economics and Social Sciences, University of Tübingen) ► Machine Learning Applications in Business and Economics - Can it Help us Understand the Relevance of Visual Product Characteristics? |
15:30 - 15:45 | BREAK |
| Spotlight Presentations |
15:45 - 15:55 | Eric Raidl / Thomas Grote ► Artificial Intelligence, Trustworthiness and Explainability |
15:55 - 16:05 | Thilo Hagendorff ► The Big Picture: Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research |
16:05 - 16:15 | Daniel Weber ► Human-robot Interface with Eye-tracking |
16:15 - 16:25 | Pablo Sanchez Martin ► Exploring Ambient Awareness in Twitter |
16:25 - 16:30 | BREAK |
16:30 - 17:15 | Ingo Steinwart (Department for Stochastics and Applications, University of Stuttgart) ► Some Thoughts towards a Statistical Understanding of Deep Neural Networks |
17:15 - 17:30 | BREAK |
17:30 - 18:15 | Claire Monteleoni (Department of Computer Science, University of Colorado Boulder) ► Deep Unsupervised Learning for Climate Informatics |
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Thursday, July 23, 2020
14:00 - 14:45 | Philipp Berens (Cluster of Excellence “Machine Learning”, University of Tübingen) ► Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance |
14:45 - 14:50 | BREAK |
| Spotlight Presentations |
14:50 - 15:00 | Jonas Ditz ► Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response |
15:00 - 15:10 | Susanne Zabel ► Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data |
15:10 - 15:20 | Paolo Mazza ► Understanding Quantum Effects in Neural Network Models through Machine Learning |
15:20 - 15:30 | Jonathan Fuhr ► Applied Causal Inference in Social Sciences and Medicine |
15:30 - 15:45 | BREAK |
15:45 - 16:30 | Stefanie Jegelka (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology) ► Keynote Lecture: Representation and Learning in Graph Neural Networks |
16:30 - 17:15 | Bedartha Goswami (Cluster of Excellence “Machine Learning”, University of Tübingen) ► Inferring Climate Variability from Patterns Hideen in Modern and Paleo Time Series Data |
17:15 - 17:30 | BREAK |
17:30 - 18:15 | Igor Lesanovsky (Department of Physics, University of Tübingen ) ► Neural Network Dynamics in Quantum Many-Body Systems |
18:15 - 18:30 | Ulrike von Luxburg, Philipp Berens Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen ► Closing Remarks |
Symposium 'Machine Learning in Science', July 7-8, 2020 -- ONLINE
Symposium 'Machine Learning in Science'
on July 7 - 8, 2020
Zoom Videoconference
Each presentation takes 30 minutes and is followed by a discussion of 15 minutes.
The discussion is followed by a 45 minutes non-public session, which will be organised as break-out-group, hence ALL participants can stay in the online conference room for the entire duration of the symposium.
Registration:
Registration is required for Non-Cluster members only. Please send an email to Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de), the link to the Zoom conference can then be provided.
Important: Participants who do not provide their full names will be excluded from the video conference by the moderators.
09:00 – 09:45 | Nicole Ludwig (Karlsruhe Institute of Technology) How Machine Learning Changes Research in Energy |
09:45 – 10:15 | Non-public session |
10:30 – 11:15 | Michal Rolínek (Max Planck Institute for Intelligent Systems, Tübingen) |
11:15 – 11:45 | Non-public session |
11:45 – 13:00 | Break |
13:00 – 13:45 | Thilo Wrona (GFZ Helmholtz-Zentrum, Potsdam) How can Machine Learning Help Us Advance Solid Earth Science? |
13:45 – 14:15 | Non-public session |
14:30 – 15:15 | Niklas Wahl (German Cancer Research Center – DKFZ, Heidelberg) How will Machine Learning change Radiotherapy? |
15:15 – 15:45 | Non-public session |
16:00 – 16:45 | Charley Wu (Harvard University, Cambridge, USA) Bridging the Gap Between Human and Machine Learning |
16:45 – 17:15 | Non-public session |
17:15 | End of 1st Day |
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09:00 – 09:45 | Christin Beck (University of Konstanz) |
09:45 – 10:15 | Non-public session |
Talk by Reinhard Diestel - January 24, 2020
Tangles: from graph minors to identifying political mindsets
Talk by Reinhard Diestel, University of Hamburg, Department of Mathematics
WHEN: Friday, 24.01.2020 at 10:00
WHERE: Lecture hall ground floor, MPI for Intelligent Systems
ABSTRACT
Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types of phenomena: of behaviour, political views, texts, or bacteria. Tangles can identify key phenomena that allow predictions of others. Tangles also offer a new paradigm for clustering in large data sets. Tangle clusters are, by necessity, fuzzy: they tell us where in a large structure a cluster lies, which key properties of data points identify it, and how the overall data set is structured with respect to these clusters. But they do this without needing, or attempting, to assign individual points to any cluster. Tangles of graphs are central to the theory of graph minors developed by Robertson and Seymour for their celebrated proof of the graph minor theorem. For many years, however, algorithmic applications of graph minor theory were largely confined to applications of tree-decompositions, an overall structure dual to the existence of large tangles. Very recently, tangles have been axiomatised in a way that makes them directly applicable to a much wider range of contexts than graphs, even outside mathematics. This talk will outline how this works, with an emphasis on the basic concepts of abstract tangle theory and how these are applicable in real-world scenarios. No knowledge of graph minor theory will be needed.
2019
Machine Learning meets Social Science, November 12, 2019
Machine Learning meets Social Science
Max Planck Institute for Intelligent Systems, Lecture Hall ground floor
Max-Planck-Ring 4, 72076 Tübingen
Registration: If you plan to attend the meeting, please register by sending an email to Sebastian Schwenk.
09:00 - 10:30 Session "ML Cluster" contenteditable="false"
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09:00 - 09:20 Introduction & Fairness in Machine Learning
Ulrike von Luxburg
09:20 - 09:40 On the Integration of Machine Learning into Healthcare
Thomas Grote
09:40 - 10:00 Explaining Neural Network Decisions with
Minimal Supervision
Zeynep Akata
10:00 - 10:20 Explainability & Explanation
Eric Raidl
10:30 - 11:00 Coffee Break
11:00 - 12:30 Session "Sociology"
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11:00 - 11:20 What does ML do? Key questions from a sociology of
technology and science perspective
Renate Baumgartner
11:20 - 11:40 (How) does ML affect the shop floor and the
sociology of work?
Werner Schmidt
11:40 - 12:00 Three Worlds of AI – How political strategies differ
Daniel Buhr
12:00 - 12:20 Classifying scientific texts with supervised learning algorithms
Steffen Hillmert
12:30 Discussion and End
1st Mini-Conference "Machine Learning in Science", July 22-23, 2019
Cluster Conference "Machine Learning in Science"
Monday, July 22, 2019 | 09:00 am - 07:00 pm | Alte Aula
Tuesday, July 23, 2019 | 09:00 am - 04:00 pm | Pfleghofsaal
Registration: If you would like to attend the meeting, please register by July 15 latest by sending an email to Sebastian Schwenk. Please indicate on which day(s) you would like to participate.
Monady, July 22, 2019
Alte Aula, Münzgasse 30, 72070 Tübingen
9:00 | Opening Remarks Ulrike von Luxburg, Philipp Berens Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen |
09:15 | Towards Neural Networks Which Probably Know When They Don't Know Matthias Hein Department of Computer Science, University of Tübingen |
10:00 | Inception Loops - Using Deep Learning to Control Biological Neurons Fabian Sinz Department of Computer Science, University of Tübingen |
10:45 | Coffee Break |
11:15 | Machine Learning for Heterogeneous and Partially Biased Data in Medicine Nico Pfeifer Department of Computer Science, University of Tübingen |
12:00 | The Art of Using t-SNE for Visualization of Very Large Data Sets Dmitry Kobak Institute for Ophthalmic Research, University of Tübingen |
12:45 | Lunch |
13:45
| Machine Learning inside Scientific Methods and Procedures Philipp Hennig Department of Computer Science, University of Tübingen |
14:30
| Dynamic Structural Equation Models in the Social and Behavioral Sciences Augustin Kelava Methods Center, University of Tübingen |
15:15
| Identifying Climate, Vegetation, and Plate Tectonic Controls on Earth’s Topography Todd Ehlers Department of Geosciences, University of Tübingen |
16:00 | Poster Session and Coffee Poster contributions, see below * |
18:00 | General Assembly of the Cluster of Excellence (non-public) |
19:00 | Speaker’s Dinner (non-public) |
Tuesday, July 23, 2019
Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen
09:00 | Language Change as a Random Walk in Vector Space Gerhard Jäger Institute of Linguistics, University of Tübingen |
09:45 | Ethics and Explainability Eric Raidl, Thomas Grote, Thilo Hagendorff Ethics & Philosophy Lab, Cluster of Excellence Machine Learning, University of Tübingen |
10:45 | Coffee Break |
11:15
| Filter ranking for neural network compression Mijung Park Department of Computer Science, University of Tübingen |
12:00
| Fairness and Interpretability in ML for Consequential Decision Making Isabel Valera Max Planck Institute for Intelligent Systems, Tübingen |
12:45 | Lunch |
13:45 | Statistical Limits of Hypothesis Testing: Do We Expect Too Much from ML? Debarghya Ghoshdastibar Department of Computer Science, University of Tübingen |
14:30 | How to Learn Predictive Conceptual Structures, including Causal Relationships, and Generate Goal-Directed Control with them? Achievements and Challenges Martin Butz Department of Computer Science, University of Tübingen |
15:15 | Machine Learning Algorithms as Tools and Models in Vision Science Felix Wichmann Department of Computer Science, University of Tübingen |
16:00 | Closing Remarks Ulrike von Luxburg, Philipp Berens Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen |
* Poster Session, July 22, 16:00 – 18:00 *
Weber, D, Kasneci E., Zell A. (Cluster Innovation Fund Project) University of Tübingen, Department of Computer Science
Valera I.1, Utz S.² (Cluster Innovation Fund Project) 1Max Planck Institute for Intelligent Systems Tübingen, ² Leibniz-Institut für Wissensmedien
Luxburg U., Wichmann F. (Cluster Innovation Fund Project) Machine learning approaches for psychophysics with ordinal comparisons University of Tübingen, Department of Computer Science
Zabel S.1, Hennig P.2, Nieselt K.1 (Cluster Innovation Fund Project) Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data University of Tübingen, 1Center for Bioinformatics Tübingen, ²Department of Computer Science
Karlbauer, M.1, Lensch H.1, Scholten T.², Butz M.1 (Cluster Innovation Fund Project) Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent Convolutional ANN University of Tübingen, 1Department of Computer Science, ²Department of Geosciences
Behrens, T.1, Schmidt, K.1, Hennig, P.², Scholten, T.1 (Cluster Innovation Fund Project) University of Tübingen, 1Department of Geosciences, ²Department of Computer Science
University of Tübingen, 1Department of Linguistics, ²Department of Computer Science
Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4 1Technische Universität München, Computational Neuroengineering Group 1 University of Tübingen, ²Department for Computer Science, ³Institute for Ophthalmic Research 4Center of advanced european studies and research
Pawlowski, T.1, Berens, P.², Kelava, A.³ University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center Kilian P. University of Tübingen, Methods Center
Klopotek M., Oettel M. University of Tübingen, Institut für Angewandte Physik
Lin SC, Oettel M. University of Tübingen, Institut für Angewandte Physik
Greco A.1, Starostin V.1, Hinderhofer A.1, Gerlach A.1, Karapanagiotis C.², Liehr S.², Kowarik S.²,
Sümer Ö.1,2, Kasneci E.1 University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and
Fuhl W., Kasneci G., Rosenstiel W., Kasneci E. University of Tübingen, Department of Computer Science Zadaianchuk A., Martius G. |
Machine Learning meets Physics, July 10, 2019
Machine Learning meets Physics
AI Building, Lecture Hall (ground floor)
Maria von Linden Str. 6, 72076 Tübingen
9:00 – 9:25: Frank Schreiber et al: “Analysis of X-ray Scattering Data Using Artificial Neural Networks”
9:25 – 9:50: Hendrik Lensch: “Deep Learning on Unstructured Point Clouds”
9:50 – 10:15: Martin Oettel: “Density functionals from machine learning”
10:15 – 10:45: Coffee break
10:45 – 11:10: Miriam Klopotek: “Variational autoencoders put up to the test in learning a statistical-mechanical model system”
11:10 – 11:35: Georg Martius: “Machine Learning for Equation Identification”
11:35 – 12:00: Andreas Zell: “ML, Physics and Robotics”
12:00 – 13:00: Discussion
TÜFFF - Tübinger Fenster für Forschung, 24. Mai 2019
TÜFFF - Tübinger Fenster für Forschung
Spitzenforschung zum Anfassen für alle Altersgruppen
WANN: Freitag, 24. Mai 2019, 15 – 22 Uhr
WO: Hörsaalzentrum der Naturwissenschaften, Auf der Morgenstelle 16
Eintritt frei
Das „Tübinger Fenster für Forschung“ (TÜFFF) bietet allgemein verständliche und interaktive Einblicke in die Tübinger Spitzenforschung. Mitmach-aktionen, Demonstrationen, Laborführungen, Vorträge, eine Informations-messe sowie ein Science Slam erwarten die interessierte Öffentlichkeit beim 4. TÜFFF an der Universität Tübingen. Durch die Aufbereitung und Präsentation aktueller Forschungsthemen für ein fachfremdes Publikum richtet sich die Veranstaltung an alle Altersgruppen.
Das Exzellenzcluster „Maschinelles Lernen“ beteiligt sich mit 8 Ständen am „Markt der Möglichkeiten“:
- Deep Capturing - Computer Vision, Prof. Hendrik Lensch
- Deep Deblurring - Computer Vision, Prof. Hendrik Lensch
- Interaktive Karte zur Bodenqualität im Raum Tübingen - Geowissenschaften, Prof. Thomas Scholten
- Was ist ein neuronales Netzwerk? – Bioinformatik, Prof. Dr. Kay Nieselt
- Antizipatives Verhalten in künstlichen neuronalen Netzen - Kognitionswissenschaften, Prof. Martin Butz
- Wie kann ein Computer lernen, Wörter in Latein, Russisch, Estnisch und Hebräisch zu beugen?
Linguistik, Prof. Harald Baayen - Vorhersage von Blickrichtungen - Neurowissenschaften,
Prof. Matthias Bethge - Briefumschlag-Computer – Theorie des maschinellen Lernens,
Prof. Ulrike Luxburg
Weitere Informationen im Programmheft und auf der Veranstaltungsseite
Symposium 'Machine Learning in Science', May 22, 2019
Symposium 'Machine Learning in Science'
May 22, 2019
Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen
09:45 | Tropical circulation: Current challenges and potential for machine learning algorithms |
10:30 | High-throughput behavioral analysis for neural circuit understanding |
11:15 | Coffee Break |
11:30 | Reverse Engineering the Early Visual System with Artificial Neural Networks |
12:15 | Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research |
Symposium 'Ethics and Philosophy of Machine Learning in Science', May 15, 2019
Symposium 'Ethics and Philosophy of Machine Learning in Science'
May 15, 2019
Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen
08:30 | Simplicity and Scientific Progress: A Topological Perspective |
09:15 | Learning Through Creativity |
10:00 | Coffee break |
10:20 | Black-Boxes, Understanding, and Machine Learning |
11:05 | Working at the margins of machine learning – the ethics of labeling |
11:50 | Inductive Bias and Adversarial Data |
12:35 | Lunch break |
13:30 - 14:30 | Invited Talk Co-Opt AI! Charting the emerging field of AI, ethics and social justiceMona Sloane, Institute for Public Knowledge, New York University, USA |
17:10 - 17:55 | Conference Room ML from a DiscO viewpoint: Compressed Sensing, Dictionary Learning and beyond |
Symposium 'Machine Learning in Science', March 18 and 25 - 27, 2019
Symposium „Machine Learning in Science“
March 18 and 25-26, 2018
Max-Planck-Gästehaus – Lecture hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen
Monday, March 18, 2019
09:30 – 10:30 | Neutrino Cosmology - Weighing the Ghost Particle with the Universe Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational |
Monday March 25, 2019
08:30 | Information Field Theory |
09:30 | Active machine learning for automating scientific discovery |
10:30 | Coffee break |
11:00 | Bayesian optimisation: nano-machine-learning |
12:00 | Robust and Scalable Learning with Graphs |
13:00 | Lunch |
15:00 | Representing and Explaining Novel Concepts with Minimal Supervision Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam |
16:00 | Coffee Break |
17:45 | Cluster Member Meeting and General Assembly (non-public) |
19:00 | Joint Dinner (by invitation) |
Tuesday, March 26, 2019
08:30 | Expressive, Robust and Accountable Machine Learning for Real-world Data |
09:30 | Algorithms of Vision: From Brains to Machines and Back |
10:30 | Coffee break |
11:00 | From Paired to Unpaired Image-to-Image Translation and Beyond |
12:00 | Face processing: Bridging Natural and Artificial Intelligence Assoc. Prof. Dr. Angela J. Yu -- University of California San Diego, USA |
13:00 | Lunch |
14:00 | From statistics to mechanisms, and back Prof. Dr. Jakob Macke -- TU München |
Machine Learning meets Law, March 19, 2019
Machine Learning meets Law, Neue Aula
9:00 Stefan Thomas: Algorithms and Antitrust: How can the law make sure that machine learning does not impede competitive freedom?
9:15 Thilo Hagendorff: Regularory Needs in the Field of AI - From Ethics to Policies
9:30 Thomas Grote: The ethics of (expert-level) algorithmic decision-making
9:45 Isabel Valera: Fairness in Machine Learning
10:00 Oliver Kohlbacher: Legal issues related to AI in medicine
10:15 Discussion as long as we want
2018
Meeting of the Cluster 'Machine Learning in Science', November 12-13, 2018
Internal Meeting of the Cluster "Machine Learning in Science": November 12-13, 2018
Meeting location: Ground floor lecture hall at the Max-Planck Institute for Intelligent Systems (directions)
Preliminary schedule:
Nov 12th
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9:00-10:00 Welcome, information & organisation
Ulrike von Luxburg and Philipp Berens
10:00-12:15 Short introductory talks of new group leaders
10:00-10:15 Jörg Stückler
10:15-10:30 Falk Lieder
10:30-11:00 Coffee break
11:00-11:15 Georg Martius
11:15-11:30 Britta Dorn
11:30-11:45 Fabian Sinz
11:45-12:00 Zhaoping Li
12:00-12:15 Gabriele Schweikert
12:15-12:30 Augustin Kelava
12:30-12:45 Michael Krone
12.45 -14:00 Lunch
14:00-15:00 Spotlights for open questions
(all PIs: please prepare exactly 1 slide (3 minutes) and send it to Alla at latest Nov 11)
15:00-15:30 Coffee break
15:30-18:00 Work phase for project teams
18:30 Dinner at Hofgut Rosenau
Nov 13th
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9:00-10:30 Discussion of open questions, directions, ideas for how
the Excellence Cluster should start and work
10:30-11:00 Coffee break
11:00-12:00 Discussion and work phase
12:00-14:00 Lunch
14:00-15:00 Presentations of project ideas and discussion
15:00-15:30 Coffee break