Veranstaltungen des Clusters
Aufgrund der aktuellen Lage rund um die Corona-Pandemie finden einige unserer geplanten Präsenzveranstaltungen im Online Format statt oder sind auf unbestimmte Zeit ausgesetzt.
Cluster Kolloquium "Maschinelles Lernen"
Seminarreihe des Exzellenzclusters "Maschinelles Lernen"
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Program folgt demnächst!
Science and Innovation Days, 29. Juni - 2. Juli 2022
Science and Innovation Days
Vom 29. Juni bis zum 2. Juli 2022 öffnen die großen Tübinger Forschungseinrichtungen ihre Türen und präsentieren interessierten Bürgerinnen und Bürgern ihre Forschung. Die Science and Innovation Days finden über die gesamte Stadt verteilt statt, das Programm ist hier zu finden. Die Veranstaltungen sind auf Deutsch.
Die Mitglieder unseres Clusters beteiligen sich mit folgenden Beiträgen:
Mittwoch, 29. Juni 2022
18:15 | Mit Eric Schulz ► Kick Off: Wissenschafft Zukunft - Wissenschaft und Gesellschaft im Dialog |
20:30 | Mit Nicole Ludwig ► Künstliche Intelligenzen der Zukunft: Fakten und Fiktionen (Lesung) Mehr Info: https://uni-tuebingen.de/de/230849#c1565249 |
Freitag, 1. Juli 2022
20:00 | Mit Robert Bamler ► Wieviel Science steckt in der Fiction? - Künstliche Intelligenz in Film und Forschung (Film und Diskussion) Mehr Info: https://uni-tuebingen.de/de/230858#c1583855 |
Samstag, 2. Juli 2022
09:30 - 10:30 | Mit Tilman Gocht ► Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 12:00 | Mit Kerstin Rau, Thomas Gläßle ► Wie gut versteht eine Maschine die Natur? Vorhersage von Bodentypen im Schönbuch Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 12:00 | Mit Georg Martius, Huanbo Sun ► Wie Roboter fühlen können - ein sensitiver Roboterfinger mit Tastsinn Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 10:30 und 11:30 - 12:00 | Für Kinder ► Bernhard Schölkopf: Warum sind Computer dumm? Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:00 - 13:00 | Mit Philipp Hennig ► Info- und Feedbackstand: Was macht Cyber Valley? Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
10:30 - 11:15 | Mit Tilman Gocht ► Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort Mehr Info: https://uni-tuebingen.de/de/230852#c1583873 |
13:15 | Für Kinder ► Andreas Geiger: Kann künstliche Intelligenz kreativ sein? ( bereits ausgebucht) Workshop für Kinder im Rahmen des Kinder-Uni-Forschungstags Mehr Info: https://uni-tuebingen.de/de/2626#c547865 |
15:15 | Für Kinder ► Andreas Geiger: Kann künstliche Intelligenz kreativ sein? Workshop für Kinder im Rahmen des Kinder-Uni-Forschungstags. Anmeldung bis 29.6 unter kinderuni @uni-tuebingen.de Mehr Info: https://uni-tuebingen.de/de/2626#c548369 |
4. Jahres-Konferenz "Machine Learning in Science", 12. + 13. Juli 2022
4. Exzellenzcluster Konferenz
"Machine Learning in Science" 2022
► Dienstag 12. Juli | 9:00 - 18:00 |
► Mittwoch 13. Juli | 9:00 - 17:00 |
------------------------------------------------------------------------------------------------------------------------------------------------------
Tagungsort: Westspitze, Eisenbahnstraße 1, 72072 Tübingen
------------------------------------------------------------------------------------------------------------------------------------------------------
Vorläufiges Programm
Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.
Dienstag, 12. Juli 2022
09:00 | Ulrike von Luxburg, Philipp Berens ► Eröffnung |
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 | Kaffeepause |
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 |
13:00 | Mittagspause |
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 und Kaffee |
19:00 | Konferenzdinner im "Freistil" (Anmeldung erforderlich) |
***************************************************************************************************************************************
Mittwoch, 13. Juli 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 | Kaffeepause |
11:15 | Devis Tuia ► KEYNOTE: Machine Learning Supporting Ecology Supporting Machine Learning |
12:15 | Richard Gao ► Simulation-based inference for discovering mechanistic models of neural population dynamics |
12:45 | Almut Sophia Köpke ► Title tbd |
13:15 | Mittagspause |
14:15 | Cluster Network Project ► Machine Learning in Education |
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 |
Philosophy of Science Meets Machine Learning (PhilML), 20. - 22. Oktober 2022
Philosophy of Science Meets Machine Learning (PhilML)
-------------------------
20. - 22. Oktober 2022
Hörsaal, Max-Planck-Gästehaus, Max-Planck-Ring 6, 72076 Tübingen
-------------------------
Mehr Informationen folgen in Kürze.
Veranstaltungs-Archiv
Hier finden Sie alle vergangenen Cluster-Veranstaltungen im Überblick.
Cluster Kolloquium "Maschinelles Lernen" - 1. Mittwoch im Monat
Seminarreihe des Exzellenzclusters "Maschinelles Lernen"
-------------------------
Mittwoch, 14:00 - 15:00, mit anschliessendem Get Together
Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen
-------------------------
PROGRAMM
-
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.
-
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. Webseite
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. -
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.
-
07.07.2021 Beyond Expectations & Better than Average: Foundations of ML Systems
Robert Williamson
Professur für "Foundations of Machine Learning Systems" an unserem
Exzellenzcluster "Maschinelles Lernen"
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.
-
09.06.2021 Learning from Trees - Modeling, Simulation, and Inference of Microbial Genome Evolution
Franz Baumdicker
Leiter der Unabhängigen Forschergruppe "Mathematical and Computational Population
Genetics", eine gemeinsame Arbeitsgruppe der beiden Tübinger Exzellenzclustern "Kontrolle
von Mikroorganismen zur Bekämpfung von Infektionen" und "Maschinelles Lernen"
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.
-
14.04.2021
Machine learning for medical image analysis and why clinicians are not using it
Christian Baumgartner
Leiter der Unabhängigen Forschergruppe "Maschinelles Lernen in der medizinischen Bildanalyse"
an unserem Exzellenzcluster "Maschinelles Lernen".
-
03.03.2021
Challenges in Renewable Energy Systems: A (mostly) Probabilistic Perspective
Nicole Ludwig
Leiterin der Early Career Forschergruppe "Maschinelles Lernen in Nachhaltigen
Energiesystemen" an unserem Exzellenzcluster "Maschinelles Lernen"
-
03.02.2021 Scalable Bayesian Inference: New Tools for New Challenges - Robert Bamler
-ONLINE-
Professur für "Data Science und Maschinelles Lernen" an unserem
Exzellenzcluster "Maschinelles Lernen"
Abstract
-
13.01.2021 Generalizing from sparse data and learning from other people - Charley Wu
-ONLINE-
Leiter der Unabhängigen Forschergruppe "Human and Machine Cognition" an unserem
Exzellenzcluster "Maschinelles Lernen"
Abstract
-
02.12.2020
Morals and Methodology - Konstantin Genin
-ONLINE-
Leiter der Unabhängigen Forschergruppe "Epistemologie und Ethik des maschinellen Lernens" an
unserem Exzellenzcluster "Maschinelles Lernen"
-
14.10.2020 ABGESAGT
-
01.04.2020 FÄLLT CORONA-BEDINGT AUS
How to be fair - The concept of fairness from a Computational Social Choice perspective
Mathematische Strukturen in der Informatik,
-
06.05.2020 (Host: Ulrike von Luxburg)
Distinguished Researcher in Data61 and Professor at Research School of Computer Science,
Australian National University, Canberra -
04.03.2020
-
05.02.2020 Learning and artificial intelligence in the quantum domain
Hans Briegel (Host: Eric Raidl)
Institut für Theoretische Physik , Universität Innsbruck & Fachbereich Philosophie,
Universität Konstanz Webseite
Abstract -
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)
Institut für Atmosphäre und Klima, ETH Zürich, Webseite
Abstract
-
04.12.2019 Explaining and Representing Novel Concepts With Minimal Supervision
Zeynep Akata
Exzellenzcluster Maschinelles Lernen, Erklärbares Machinelles Lernen, Universität Tübingen
Webseite
-
21.11.2019 Biomarker Discovery in Clinical Time Series
Karsten Borgwardt
Department für Biosystems Science and Engineering, ETH Zuerich. -
06.11.2019 Gaussian Process emulation of tsunami and climate models
Serge Guillas (Host: )
University College London / The Alan Turing Institute. Webseite
Abstract
-
03.07.2019 Machine Learning, Neuroscience, and Spiking Neural Networks
Robert Legenstein (Host: Harald Baayen)
Institut für Grundlagen der Informationsverarbeitung, TU Graz, Österreich
Webpage
Abstract
Workshop „AI and ML Research and Democracy”, 2. - 4. April 2022
PhD Workshop "Artificial Intelligence and Machine Learning Research and Democracy" mit öffentlicher Abschlussveranstaltung
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
2. - 4. April 2022 an der Universität Tübingen
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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“ am 4. April um 18:30
Zum Abschluss des Workshops findet am 4. April ab 18:30 Uhr eine öffentliche Diskussionsrunde zu "KI und Demokratie" in der "Westspitze" in Tübingen statt.
Zu dieser Veranstaltung sind alle Interessierten herzlich eingeladen.
Anmeldung über folgenden Link: ai-and-democracy-workshop.de/podium
Workshop "Introduction to Machine Learning", 30. März 2022
Workshop "Introduction to Machine Learning"
Das Machine Learning ⇌ Science Colaboratory lädt zu einem praxisorientierten Präsenz-Workshop über die Einführung in das Maschinelle Lernen ein.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Mittwoch, 30. März 2022, 9:00 bis 13:00
Die Veranstaltung findet vor Ort statt in der Maria-von-Linden-str. 6, 72076 Tübingen, Hörsaal (Erdgeschoss, Raum 00-28/A7)
Anmeldung:
Hier geht es zum Anmeldeformular
Bitte beachten, dass die Teilnehmerzahl auf 12 Personen begrenzt ist! Teilnehmer werden etwa eine Woche vor der Veranstaltung von uns benachrichtigt.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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
Workshop "Philosophy of Science Meets Machine Learning", 9. - 12. November 2021
Workshop "Philosophy of Science Meets Machine Learning"
Der Workshop findet in Präsenz in Tübingen statt
am 09.11. + 10.11. in der Alten Aula (Münzgasse 30)
am 11.11. + 12.11. im Max Planck Institute for Intelligent Systems (Max-Planck-Ring 4)
Der Workshop wird organisiert von der Arbeitsgruppe 'Ethik und Philosophie der Künstlichen Intelligenz' des Exzellenzclusters 'Machine Learning: Neue Perspektiven für die Wissenschaft' an der Universität Tübingen (Workshop-Veranstalter: Thomas Grote, Thilo Hagendorff, Eric Raidl).
Anmeldung: Die Plätze sind begrenzt. Gäste melden sich bitte an über: rebigtim @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.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Programm
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
09:00 - 09:50 | 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 |
Vortrag Claire Vernade - 30. September 2021
| Bandit learning with delays in Non-stationary environments |
Claire Vernade
WANN: Donnerstag, 30.09.2021 um 9:00
WO: Hybrid-Veranstaltung:
Claire hält den Vortrag im Hörsaal der Maria-von-Linden-Straße 6 (EG). Aufgrund der aktuellen Covid-Beschränkungen sind die Plätze begrenzt. Cluster-PIs können vor Ort im Hörsaal teilnehmen - bei Interesse bitte bei Elena Sizana anmelden. Es gilt die 3G-Regel (vollständig geimpft, getestet oder genesen).
Alle anderen laden wir ein, über Zoom teilzunehmen: 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", 20.-22. September 2021, ONLINE
Workshop "Simulation-based Inference for scientific discovery"
Der Workshop findet virtuell auf Zoom statt.
Der Workshop wird gemeinsam organisiert vom ML⇌Science Colaboratory, der Machine Learning in Science Arbeitsgruppe (Jakob Macke) und Helmholtz AI.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Anmeldung:
Deadline für die Anmeldung ist der 31. August, hier geht es zum Anmeldeformular
Please note, 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.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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:
| Programm
20. September | Using simulators for discovery, introduction to conditional density estimation.
|
21. September | How does neural simulation-based inference work?
|
22. September | Applying the sbi toolbox to your problem. Pitfalls, tricks and opportunities!
|
3. Jahres-Konferenz "Machine Learning in Science", 12. + 13. Juli 2021
3. Exzellenzcluster Konferenz
"Machine Learning in Science" 2021
► Montag 12. Juli | 14:00 - 18:00 | Registrierungslink
anschliessendes Online Theater um 19:30
► Dienstag 13. Juli | 14:00 - 18:00 | Registrierungslink
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Die Konferenz findet virtuell statt über Zoom.
Eine Anmeldung ist notwendig, für jeden Tag separat, Registrierungslinks s.o.
Das Theaterstück am Montagabend ist für die Öffentlichkeit zugänglich, weitere Informationen hier. Das Stück wird in englischer Sprache gespielt und auf Youtube live gestreamt. Im Anschluss gibt es eine Diskussion mit den Schauspielern und einigen Forschenden unseres Clusters. Eine Anmeldung ist nicht erforderlich.
Links:
Theaterstück https://tinyurl.com/SiliconWoman
Anschliessende Diskussion auf Zoom https://zoom.us/j/91670801978
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
PROGRAMM
Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.
Montag, 12. Juli 2021
14:00 - 14:15 | Ulrike von Luxburg, Philipp Berens ► Eröffnung |
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 | PAUSE |
15:30 - 16:00 | Konstantin Genin ► Clinical Equipoise and Causal Discovery |
16:00 - 16:45 | Spotlight Präsentationen Innovation Fund Projekte des Exzellenzclusters "Maschinelles Lernen" |
16:00 - 16:10 ► Machine Learning Approaches for Psychophysics with Ordinal Comparisons | |
16:10 - 16:20 ► Interpretable Spatial Machine Learning for Environmental Modelling | |
| 16:20 - 16:30 ► Human-Robot Interface with Eye-Tracking |
| 16:30 - 16:40 ► Counterfactual Explanations of Decisions of Deep Neural Networks with Applications in Medical Diagnostics |
16:45 - 17:00 | PAUSE |
17:00 - 17:30 | Spotlight Präsentationen Innovation Fund Projekte des Exzellenzclusters "Maschinelles Lernen" |
17:00 - 17:10 ► Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data | |
17:10 - 17:20 ► Modelling Behavioral Responses to Emotional Cues in Sports - A Bayesian Approach | |
17:20 - 17:30 ► 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 | PAUSE |
19:30 - 20:15 | Theater ► Silicon Woman - the Singing Cyborg |
***************************************************************************************************************************************
Dienstag, 13. Juli 2021
14:00 - 14:50 | Keynote Lecture ► Machine Learning and the Physical World |
14:50 - 15:20 | Samira Samadi ► Socially Fair k-Means Clustering |
15:20 - 15:30 | PAUSE |
15:30 - 16:30 | Spotlight Präsentationen |
| 15:30 - 15:40 ► Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent CNN |
| 15:40 - 15:50 ► Extracting Expertise from Tweets: Exploring the Boundary Conditions of Ambient Awareness |
| 15:50 - 16:00 ► Enhancing Machine Learning of Lexical Semantics with Image Mining |
| 16:00 - 16:10 ► Applied Casual Inference in Social Sciences and Medicine |
| 16:10 - 16:20 ► Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response |
| 16:20 - 16:30 ► Analytic Classical Density Functionals from an Equation Learning Network |
16:30 - 16:45 | PAUSE |
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 |
Workshop "Philosophy of Medical AI", 08.-09. Okt 2020 -- ONLINE
Virtual Workshop on the Philosophy of Medical AI
► Donnerstag, 08. Oktober | 09:30 - 17:30
► Freitag, 09. Oktober | 10:00 - 16:00
Anmeldung
Der Workshop ist öffentlich zugänglich, eine Anmeldung ist nicht erforderlich.
Organisation
Thomas Grote; Ethics and Philosophy Lab; Cluster of Excellence “Machine Learning: New Perspectives for Science”
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Der Workshop findet virtuell auf Zoom statt.
Link zum Meetig am DONNERSTAG Meeting-ID: 977 8903 0792
Link zum Meeting am FREITAG Meeting-ID: 990 9618 3434
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Die jüngsten Fortschritte im Bereich des Deep Learning haben das Interesse an der Anwendung von KI-Systemen im Gesundheitswesen weiter gesteigert. Eine Vielzahl hochkarätiger Studien zeigt, wie vielfältig die Möglichkeiten sind, die KI für verschiedene Zweige der Medizin bietet: Sie reichen von der Verbesserung medizinischer Diagnosen über die rechtzeitige Vorhersage von Gesundheitsrisiken bis hin zur Entdeckung neuer Medikamente. Gleichzeitig gibt es Befürchtungen, dass die Unvollkommenheit der gegenwärtigen KI-Systeme strukturelle Missstände im Gesundheitssystem verstetigen könnten oder sogar neue ethische Probleme schaffen könnten. Ziel dieses Workshops ist es, über die Chancen und Herausforderungen des Einsatzes von KI in der Medizin nachzudenken. Dazu bringt der Workshop Wissenschaftsphilosoph*innen, Medizinethiker*innen sowie Forscher*innen aus den Bereichen Maschinelles Lernen oder Bioinformatik zusammen.
Donnerstag, 08. Oktober 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 Machine Learning |
| MITTAGSPAUSE |
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 |
| PAUSE |
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 |
***************************************************************************************************************************************
Freitag, 09. Oktober 2020
10:00 - 10:50 | Chris Burr (Alan Turing Institute) Responsible Innovation and Digital Psychiatry |
11:00 - 11:50 | Rune Nyrup (University of Cambridge) Value Transparency in Science and Machine Learning |
| MITTAGSPAUSE |
13:00 - 13:50 | Emily Sullivan (University of Eindhoven) Opacity in Medical Explanations: Is AI Special? |
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, 25. September 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.
_______________________________________________________________________________________________________________________________________________________________________________________________
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.
2. Jahres-Konferenz "Machine Learning in Science", 21. - 23. Juli 2020 -- ONLINE
2. Exzellenzcluster Konferenz
"Machine Learning in Science" 2020
► Dienstag 21. Juli | 14:00 - 17:45
► Mittwoch 22. Juli | 14:00 - 18:15
► Donnerstag 23. Juli | 14:00 - 18:30
Programmänderung, Donnerstag 23. Juli: Der Vortrag von Manfred Claassen um 14:00 Uhr muss leider abgesagt werden. Phillip Berens übernimmt dankenswerterweise kurzfristig mit einem Vortrag zum Thema "Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance" contenteditable="false".
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Die Konferenz findet virtuell auf Crowdcast mit einem Live-Stream auf Youtube statt.
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Anmeldung
Die Konferenz ist zugänglich für die Öffentlichkeit. Um per Chat an den Diskussionen teilnehmen zu können, muss man sich über Crowdcast anmelden - Kamera und Mikrofon der Teilnehmer bleiben dabei ausgeschaltet. Einfach an jedem Konferenztag auf den entsprechenden Link auf unserem Crowdcast-Profil klicken. Zuerst die E-Mail-Adresse angeben und dann den vollständigen Namen (Vor- und Nachname).
Wer die Vorträge nur verfolgen möchten, kann dies über Youtube tun, eine Registrierung ist nicht erforderlich. Hier sind die Livestreams aller Vorträge zu finden.
Jeder Vortrag dauert 30 Minuten plus 15 Minuten Diskussion, jede Spotlight-Präsentation 5 Minuten plus 5 Minuten Diskussion.
Wichtig: Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.
Dienstag, 21. Juli 2020
14:00 - 14:15 | Ulrike von Luxburg, Philipp Berens ► Eröffnung ► Grußworte |
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 ► Learning Decision Tress Recurrently through Communication |
15:45 - 16:00 | PAUSE |
| Spotlight Präsentationen |
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 | PAUSE |
17:00 - 17:45 | Jakob Macke ► Training Neural Networks to Identify Mechanistic Models of Neural Networks |
***************************************************************************************************************************************
Mittwoch, 22. Juli 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 (Fachbereich Wirtschaftswissenschaft, Universität Tübingen) ► Machine Learning in Business and Economics - Can it Help us Understand the Relevance of Visual Product Characteristics? |
15:30 - 15:45 | PAUSE |
| Spotlight Präsentationen |
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 | PAUSE |
16:30 - 17:15 | Ingo Steinwart (Institut für Stochastik und Anwendungen, Universität Stuttgart) ► Some Thoughts towards a Statistical Understanding of Deep Neural Networks |
17:15 - 17:30 | PAUSE |
17:30 - 18:15 | Claire Monteleoni (Department of Computer Science, University of Colorado Boulder) ► Deep Unsupervised Learning for Climate Informatics |
***************************************************************************************************************************************
Donnerstag, 23. Juli 2020
14:00 - 14:45 | Philipp Berens (Exzellencluster “Maschinelles Lernen”, Universität Tübingen) ► Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance |
14:45 - 14:50 | PAUSE |
| Spotlight PräsentationenInnovation Fund Projekte des Exzellenclusters “Maschinelles Lernen” |
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 | PAUSE |
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 (Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen) ► Inferring Climate Variability from Patterns Hidden in Modern and Paleo Time Series Data |
17:15 - 17:30 | PAUSE |
17:30 - 18:15 | Igor Lesanovsky Fachbereich Physik, Universität Tübingen ► Neural Network Dynamics in Quantum Many-Body Systems |
18:15 - 18:30 | Ulrike von Luxburg, Philipp Berens Sprecher Exzellenzcluster “Maschinelles Lernen”, Universität Tübingen ► Closing Remarks |
Symposium "Machine Learning in Science", 7. - 8. Juli 2020 -- ONLINE
Symposium "Machine Learning in Science"
am 7. und 8 Juli 2020
Zoom Videokonferenz
Jeder Vortrag dauert 30 Minuten, gefolgt von einer Diskussion von 15 Minuten.
An die Diskussion schließt sich eine 45-minütige nicht-öffentliche Sitzung an, die als Break-out-Gruppe organisiert ist, so dass ALLE Teilnehmer während der gesamten Dauer des Symposiums im Online-Konferenzraum bleiben können.
Anmeldung:
Die Anmeldung ist nur für Nicht-Cluster-Mitglieder erforderlich. Bitte senden Sie eine E-Mail an Sebastian Schwenk (sebastian.schwenk), der Link zur Zoom-Konferenz wird dann zur Verfügung gestellt. @uni-tuebingen.de
Wichtig: Teilnehmer, die nicht ihren vollständigen Namen angeben, schließt der Moderator von der Videokonferenz aus.
09:00 – 09:45 | Nicole Ludwig (Karlsruhe Institute of Technology) How Machine Learning Changes Research in Energy |
09:45 – 10:15 | nicht-öffentliche Sitzung |
10:30 – 11:15 | Michal Rolínek (Max Planck Institute for Intelligent Systems, Tübingen) Machine Learning and Combinatorial Optimization |
11:15 – 11:45 | nicht-öffentliche Sitzung |
11:45 – 13:00 | PAUSE |
13:00 – 13:45 | Thilo Wrona (GFZ Helmholtz-Zentrum, Potsdam) How can Machine Learning Help Us Advance Solid Earth Science? |
13:45 – 14:15 | nicht-öffentliche Sitzung |
14:30 – 15:15 | Niklas Wahl (German Cancer Research Center – DKFZ, Heidelberg) How will Machine Learning change Radiotherapy? |
15:15 – 15:45 | nicht-öffentliche Sitzung |
16:00 – 16:45 | Charley Wu (Harvard University, Cambridge, USA) Bridging the Gap Between Human and Machine Learning |
16:45 – 17:15 | nicht-öffentliche Sitzung |
17:15 | Ende Tag 1 |
*********************************************************************************************************************************************************
09:00 – 09:45 | Christin Beck (University of Konstanz) Learning the Language of the Past: Historical Linguistics, Natural Language Processing and Machine Learning |
09:45 – 10:15 | nicht-öffentliche Sitzung |
Vortrag Reinhard Diestel - 24. Januar 2020
Tangles: from graph minors to identifying political mindsets
Vortrag von Reinhard Diestel, Universität Hamburg, Fachbereich Mathematik
WANN: Freitag, 24.01.2020 um 10:00
WO: Hörsaal am MPI for Intelligente Systeme, Erdgeschoss
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.
1. Mini-Konferenz "Machine Learning in Science", 22. - 23. Juli 2019
Exzellenzcluster Konferenz "Machine Learning in Science"
Montag, 22.07.2019 | 09:00 Uhr - 19:00 Uhr | Alte Aula
Dienstag, 23.07.2019 | 09:00 Uhr - 16:00 Uhr | Pfleghofsaal
Anmeldung: Bitte melden Sie sich bis spätestens 15.07.2019 verbindlich zu der Tagung an, E-Mail an Sebastian Schwenk. Bitte geben sie an, an welchen Tagen Sie teilnehmen möchten.
Montag, 22.07.2019
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 | Kaffeepause |
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 | Mittagessen |
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 Posterbeiträge siehe unten * |
18:00 | Mitgliederversammlung Exzellenzcluster (nicht-öffentlich) |
19:00 | Speaker’s Dinner (nicht-öffentlich) |
Dienstag, 23.07.2019
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 | Kaffeepause |
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 | Mittagessen |
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, 10. Juli 2019
Machine Learning meets Physics
AI Gebäude, Hörsaal (EG)
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: Kaffeepause
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: Diskussion
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 "Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft", 15. Mai 2019
„Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft“
15. Mai 2019
08:30 | Simplicity and Scientific Progress: A Topological Perspective |
09:15 | Learning Through Creativity |
10:00 | Kaffeepause |
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 | Mittagspause |
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 | KONFERENZRAUM |
Symposium "Machine Learning in Science", 18. und 25. - 27. März 2019
Symposium „Machine Learning in Science“
18. und 25. - 26. März 2019
Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen
Montag, 18. März 2019
09:30 – 10:30 | Neutrino Cosmology - Weighing the Ghost Particle with the Universe Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational |
Montag, 25. März 2019
08:30 | Information Field Theory |
09:30 | Active machine learning for automating scientific discovery |
10:30 | Kaffeepause |
11:00 | Bayesian optimisation: nano-machine-learning |
12:00 | Robust and Scalable Learning with Graphs Prof. Dr. Stephan Günnemann -- TU München |
13:00 | Mittagessen |
15:00 | Representing and Explaining Novel Concepts with Minimal Supervision Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam |
16:00 | Kaffeepause |
17:45 | Konstituierende Clustersitzung und Mitgliederversammlung (nicht-öffentlich) |
19:00 | Gemeinsames Abendessen (nicht öffentlich) |
Dienstag, 26. März 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 | Kaffeepause |
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 | Mittagessen |
14:00 | From statistics to mechanisms, and back Prof. Dr. Jakob Macke - TU München |
Machine Learning meets Law, 19. März 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
Erstes Treffen des Cluster "Machine Learning in Science", 12.-13. November, 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
=========
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
========
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