Events of the Cluster of Excellence
Due to the current situation associated with the corona crisis, almost all our currently planned face-to-face events will either take place in an online format or have been temporarily put on hold.
Cluster Colloquium "Machine Learning" -- 1° Wednesday of the month -- ONLINE
Seminar Series of the Cluster for Excellence
"Machine Learning"
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Wednesday, 2:00 - 3:00 pm
The Colloq will take place virtually on Zoom
Link to Colloq
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PROGRAM
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12.05.2021 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"
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06.10.2021 Ian 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
Website
3rd Annual Conference "Machine Learning in Science", July 12 + 13, 2021
3rd Cluster Conference
"Machine Learning in Science" 2021
► Monday, July 12 |
► Tuesday, July 13 |
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Info on the conference format and program will be announced here soon.
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Workshop "Philosophy of Science Meets ML", POSTPONEDto November 2021
Workshop "Philosophy of Science Meets Machine Learning"
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The workshop planned for April 27 - 30, 2021, is now scheduled for
November 2021
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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
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.
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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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
-ONLINE-
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
-ONLINE-
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
Mathematical Structures in Computer Science, Department of Computer Science,
University of Tübingen.
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06.05.2020 CANCELLED
Distinguished Researcher in Data61 and Professor at Research School of Computer Science,
Australian National University, Canberra
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04.03.2020
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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
Department of Biosystems Science and Engineering, ETH Zuerich.
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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
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
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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".
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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.schwenk), the link to the Zoom conference can then be provided. @uni-tuebingen.de
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.
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"
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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
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