Events of the Cluster


Due to the current situation associated with the corona pandemic, some of our currently planned events may take place in an online format. Please refer to the announcement for each event.

Cluster Colloquium "Machine Learning"

Seminar Series of the Cluster of Excellence "Machine Learning"

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Wednesdays  | 2.00 - 3.00 pm | followed by a get-together

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PROGRAM

    01.03.2023   Prof. Pavan Ramdya
                            Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
                            Director of the Neuroengineering Laboratory Website

Workshop "Machine Learning MEETS Qualitative Social Sciences", January 25, 2023

Workshop "Machine Learning meets Qualitative Social Sciences"
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Wednesday, January 25, 2023 | 09:00 - 1:30 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Christoph Bareither, Thomas Thiemeyer

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until January 20, 2023.

Program [PDF]

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Panel 1: Human-ML-Relationships in Scientific Practice

09:00 -  09:30       Human-ML-Relationships from the Perspective of Digital Anthropology
Christoph Bareither (Institute of Historical and Cultural Anthropology, University of Tübingen)
09:30 - 10:00 Data
Robert C. Williamson (Department of Computer Science, University of Tübingen)
10:00 - 10:30 Improving Teamwork between Humans and ML
Samira Samadi (Max Planck Institute for Intelligent Systems, Tübingen)
10:30 - 11:00 ML in (Scientific) Contexts: Ethical, Societal, and Philosophical Questions
Regina Ammicht Quinn, Jessica Heesen, Wulf Loh (International Center for Ethics in the Sciences and Humanities (IZEW), University of Tübingen)
11:00 - 11:30 Coffee Break


Panel 2: Machine Learning and Society

11:30 - 12:00 Morals and Methodology
Konstantin Genin (Cluster of Excellence Machine Learning for Science, University of Tübingen)
12:00 - 12:30 The Social Foundations of Computation
Moritz Hardt (Max Planck Institute for Intelligent Systems, Tübingen)
12:30 - 13:00 ML in Tübingen
Ulrike von Luxburg (Department of Computer Science, University of Tübingen)
Thomas Thiemeyer (Institute of Historical and Cultural Anthropology, University of Tübingen)
13:00 - 13:30 General Discussion & Concluding Remarks
13:30 - Lunch organized by the Excellence Cluster ML for Science

Workshop "Machine Learning MEETS Geosciences", January 25, 2023

Workshop "Machine Learning meets Geosciences"
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Wednesday, January 25, 2023 | 02:00 - 5:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Thomas Scholten, Todd Ehlers

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until 20. Januar 2023

Program [PDF]

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13:30 - 14:00 Arrival and Lunch

Panel 1: Current Research Topics in Geosciences

14:00 - 14:15 Causal Explanations for Model Interpretation in Digital Soil Mapping
Nafiseh Kakhani (Department of Geosciences, University of Tübingen)
14:15 - 14:30 Using ML for Inversion of Mechanistic Forward Models in Geophysics
Reinhard Drews (Department of Geosciences, University of Tübingen)
14:30 - 14:45 ISOCLIM: Exploring Isotopic Constraints on Future Climate Variability
Kira Rehfeld (Department of Geosciences, University of Tübingen)
14:45 - 15:00 Discussion: Additional Statements from Geosciences
15:00 - 15:15 Coffee Break


Panel 2: Current Research Topics in Machine Learning

15:15 - 15:30 Inference with Computational Uncertainty
Philipp Hennig (Department of Computer Science, University of Tübingen)
15:30 - 15:45 Machine Learning in Science
Jakob Macke (Department of Computer Science, University of Tübingen)
15:45 - 16:00 Modeling SpatioTemporal Dynamics
Martin Butz (Department of Computer Science, University of Tübingen)
16:00 - 16:15 Discussion: Additional Statements from ML
16:15 - 17:00 General Discussion & Concluding Remarks
17:00 - Snacks and Refreshments

 

Workshop "Machine Learning MEETS Machine Learning“, February 1, 2023

Workshop "Machine Learning meets Machine Learning
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Wednesday, February 1, 2023 | 11:00 am - 02:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Philipp Hennig, Matthias Hein

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

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Workshop "Machine Learning MEETS Physics", February 1, 2023

Workshop "Machine Learning meets Physics"
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Wednesday, February 01, 2023 | 01:00 - 05:00 pm

Venue: Auf der Morgenstelle 10, Building C, Room 7E02

Organizers: Martin Oettel, Frank Schreiber

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

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Workshop "Machine Learning MEETS Quantitative Social Sciences", February 8, 2023

Workshop "Machine Learning meets Quantitative Social Sciences"
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Wednesday, February 8, 2023 | 08:25 - 04:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Dominik Papies, Augustin Kelava

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until February 3, 2023.

Programm [PDF]

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08:00                               Arrival and Check-in with Coffee
08:25 - 08:30       Welcome
Augustin Kelava (Methods Center), Dominik Papies (Department of Economics)
08:30 - 08:45      Estimating causal effects with double/debiased machine learning - a
method evaluation
Jonathan Fuhr (School of Business and Economics), Dominik Papies, Philipp Berens
08:45 - 09:00       Machine learning meets causal inference: An econometric perspective
Michael Knaus (School of Business and Economics)
09:00 - 09:15       Random forests and double machine learning: Applications and extensions in labour economics
Martin Biewen (School of Business and Economics), Philipp Kugler, Pascal Erhardt
09:15 - 09:30     The use of machine learning techniques in Psychometrics: A necessary
transition for modern data designs
Holger Brandt (Methods Center), Roberto Faleh, Patrick Schmidt, Zachary Roman
09:30 - 09:45     Optimized estimation of nonparametric causal mediation models via
machine learning procedures
Roberto Faleh (Methods Center), Holger Brandt
09:45 - 10:00     Discussion and Wrap-up 1
10:00 - 10:25     Coffee Break
10:25 - 10:40     Generalized Mincer-Zarnowitz regressions
Patrick Schmidt (Methods Center), Holger Brandt
10:40 - 10:55     Efficient estimation and forecasting of heterogeneous latent variable
models for intensive longitudinal data
Augustin Kelava (Methods Center)
10:55 - 11:10     Data-driven performance analysis utilizing machine learning techniques: identifying and improving players’ key performance factors in elite soccer
Oliver Höner (Institute of Sports Science), Gabriel Anzer, Pascal Bauer, Benedikt Hosp, Augustin Kelava, Pascal Kilian, Daniel Leyhr, Florian Schultz
11:10 - 11:25     The Dirichlet-Horseshoe - A new shrinkage prior for both dense and sparse signal detection
Lukas Fischer (Methods Center), Michael Nagel, Tim Pawlowski, Augustin Kelava
11:25 - 11:40     A deep learning model for complex interpretable and identified factor analysis – Evaluation on talent structure of youth soccer players
Pascal Kilian (Methods Center), Daniel Leyhr, Oliver Höner, Augustin Kelava
11:40 - 11:55     Discussion and Wrap-up 2
11:55 - 12:40     Lunch
12:40 - 12:55     Identifying distinct subgroups of suicidal ideations: A pre-registered ecological momentary assessment study in psychiatric patients
Roman Zachary (Methods Center)
12:55 - 13:10     Explaining relationships between academic documents using generative transformer models
Aseem Behl (School of Business and Economics)

13:10 - 13:25    

The face of trustworthiness: Can a machine detect valid cues of trustworthy behavior in human faces?
Stefan Mayer (School of Business and Economics), Agnes Bäker, Jan Landwehr, Martin Natter
13:25 - 13:40     Analysing structures of the scientific discourse with NLP tools
Steffen Hillmert (Institute of Sociology)
13:40 - 13:55     Discussion and Wrap-up 3
13:55 - 14:15     Coffee Break
14:15 - 14:30     Sports as a behavioral (economics) lab
Tim Pawlowski (Institute of Sports Science)
14:30 - 14:45     Machine learning methods for large survey data in social sciences: Challenges, solutions, and future directions
Kou Murayama (Hector Research Institute of Education Sciences and Psychology), Rosa Lavelle-Hill
14:45 - 15:00     Machine learning for modelling, assessing and supporting educational processes
Thorsten Bohl (Tübingen School of Education), Xiaobin Chen, Ulrike Cress, Peter Gerjets, Andreas Lachner, Detmar Meurers, Kou Murayama, Ulrich Trautwein
15:00 - 15:15     Quantification of stock risk premia using theory-based/option-implied methods and machine learning approaches, and testing conditional factor asset pricing models with the help of ML-methods Steffen Hillmert
Joachim Grammig (School of Business and Economics), Constantin Hanenberg, Christian Schlag, Jantje Sönksen
15:15 - 15:30     Combining ML and psychometrics is a two-way street – Example for mixed effects in machine learning
Pascal Kilian (Methods Center), Sangbaek Ye, Augustin Kelava
15:30 - 15:45     Discussion and Wrap-up 4
15:45 - 16:00     What follows next
16:00 -     Snacks and Refreshments

 

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Workshop "Machine Learning MEETS Neurosciences", February 15, 2023

Workshop "Machine Learning meets Neurosciences"
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Wednesday, February 15, 2023 | 1:30 - 4:00 pm

Venue: TBA

Organizers: Tobias Kaufmann, Jakob Macke

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de).

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Focus Session "Physics meets ML" at the DPG Spring Meeting, March 27, 2023

Spring Meeting of the German Physical Society (DPG), Section Condensed Matter Section
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Tuesday, March 27, 2023

Venue: Technische Universität Dresden - Campus Südvorstadt, Bergstraße 64, 01069 Dresden

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From 26 March to 31 March 2023 the DPG Spring Meeting of the Condensed Matter Section (SKM) will take place on the campus of the Technical University Dresden.

A Focus Session "Physics meets ML", organized by Sabine Andergassen and Moritz Helas, Martin Gärttner and Markus Schmitt, will be held on March 27, 2023. The corresponding tutorial will take place on March 26, 2023

PROGRAM

Machine learning for complex quantum systems
Stefanie Czischek, University of Ottawa
Marcello Dalmonte, ICTP Trieste
Florian Marquardt, MPI & University of Erlangen-Nürnberg*
Christof Weitenberg, University of Hamburg

Understanding machine learning as complex interacting systems
Elena Agliari, Roma “La Sapienza”
Marc Mezard, Bocconi University
Manfred Opper, TU Berlin*
Cengiz Pehlevan, Harvard University
Zohar Ringel, Hebrew University of Jerusalem*

*Tutorial Lecturer

Spring School on Probabilistic Numerics, March 27 - 29, 2023

Spring School on Probabilistic Numerics
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Monday, March 27 - Wednesday, March 29, 2023

Venue: Ernst von Sieglin Hörsaal in Castle Schloss Hohentübingen, University of Tübingen

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The first ever Probabilistic Numerics Spring School and Research Workshop consists of two days of (in-person) lectures, keynotes, and tutorial sessions, from the 27th to the 28th of March. It will be held in English, and is aimed at graduate students, researchers, and professionals interested in probabilistic numerical methods.The school features lectures and keynotes by leading experts, and hands-on code tutorials.

The school will be followed by a workshop on the 29th of March, 2023. The workshop offers a stage for advances in the probabilistic computation, by researchers working in the field.

For more information visit the Workshop webpage

 

"Explainability in Machine Learning", March 28 - 29, 2023

Workshop on "Explainability in Machine Learning"

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  Tuesday March 28 and Wednesday March 29, 2023

Venue: Alte Aula, Münzgasse 30, 72070 Tübingen

Workshop organizers: Zeynep Akata, Stephan Alaniz, Christian Baumgartner, Almut Sophia Koepke, Massimiliano Mancini, Seong Joon Oh

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Deep learning has enabled major advances in machine learning. However, the deployment of deep learning frameworks in settings that are safety-critical or that impact society requires their decision-making to be explainable. This is fundamental for building trustworthy and user-oriented machine learning models. The aim of this workshop is to generate awareness around explainability in machine learning which is a topic of growing interest. Furthermore, we aim to encourage interdisciplinary interaction and collaboration between researchers from the University of Tübingen and other international institutions that work on different aspects of explainability, in particular in the context of computer vision.

  Program: The workshop will contain both keynote talks from known researchers in the field as well as invited talks and spotlight presentations of recent advancements in the field of explainability.

► Preliminary program and more information: https://www.eml-unitue.de/eml-workshop

 

5th Annual Conference "Machine Learning in Science", July 11 + 12, 2023

5th Cluster Conference
"Machine Learning in Science" 2023

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► Tuesday, July 11

► Wednesday, July 12

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Venue: Freistil, Wöhrdstraße 25, 72072 Tübingen

More information coming soon!
 

AITE Closing Conference, October 24 -26, 2023

"Artificial Intelligence, Trustworthiness and Explainability"
(PhilML2023)  

Closing Conference

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  Tuesday October 24 to Thursday October 26, 2023

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Saeedeh Babaii, Sara Blanco, Oliver Buchholz, Eric Raidl

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The project „Artificial Intelligence, Trustworthiness and Explainability (AITE)“, funded by the Baden-Württemberg Stiftung, is a joint project of our Cluster "Ethics and Philosophy Lab" and the "International Centre for Ethics in the Sciences and Humanities" (IZEW) at the University of Tübingen.

Presently, it remains opaque why machine learning systems (ML) decide or answer as they do. When an image classifier says "this is a train", does it 'recognise' the train or only the rails, or something totally different? How can we be sure that it decides as it does for the right reasons? This problem is at the heart of at least two debates: Can we trust artificial intelligent (AI) systems? And if so, on which basis? Would an explanation of the decision help our understanding and ultimately foster trust? And if so, what kind of explanation? These are the central questions being adressed in the AITE project.

At the AITE closing conference scientists working on this project present their work, exchange with experts in the field and other interested scholars, and engage with the wider public in a panel discussion.

More information on the  conference website
 

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

Workshop "Machine Learning MEETS Medicine", January 18, 2023

Workshop "Machine Learning meets Medicine"
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Wednesday, January 18, 2023 | 1:00 - 4:00

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Manfred Claassen, Sergios Gatidis

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)

Program [PDF]

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12:30 – 13:00                     

Arrival and Lunch

13:00 – 14:30

Impulse Pitches
(6 minutes presentation, 4 minutes discussion each)  
Juliane Walz (Medical Faculty, Peptide-based Immune Therapy)

Josef Leibold (Medical Faculty, Functional Immunogenomics)

Bettina Weigelin (Werner Siemens Imaging Center, Preclinical Imaging of the Immune System)

Michael Bitzer / Pavlos Missios (Medical Faculty, Gastroenterology)

Daniela Thorwarth (Medical Faculty, Biomedical Physics)

Tobias Kaufmann (Medical Faculty, Computational Psychiatry)

Christian Baumgartner (Machine Learning Cluster of Excellence, Machine Learning in Medical Image Analysis)

Carsten Eickhoff (Medical Faculty, Medical Informatics)

14:30 – 16:00

Discussion about Strategic Directions

16:00 –

Snacks and Refreshments

                  

Workshop "Machine Learning MEETS Linguistics", January 11, 2023

Workshop "Machine Learning meets Linguistics"
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Wednesday, January 11, 2023 | 2:00 - 5:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Michael Franke, Detmar Meurers

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)

Programm [PDF]

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13:30 – 14:00

Arrival and Lunch

14:00 – 14:10

Workshop Goals: Reiterate Mission Statement  
Michael Franke, Detmar Meurers (Department of Linguistics, University of Tübingen)

14:10 – 14:25

Explainability in Deep Learning through Communication

Zeynep Akaty (Department of Computer Science, University of Tübingen)

14:25 – 14:40

Current Collaborative Projects with ML Involving the Quantitative Linguistics Group

Harald Baayen (Department of Linguistics, University of Tübingen)

14:40 – 14:55

Event-Predictive Language Grounding

Martin Butz (Department of Computer Science, University of Tübingen)

14:55 – 15:10

Probabilistic Pragmatics Meets (Needs?) ML

Michael Franke (Department of Linguistics, University of Tübingen)

15:10 – 15:30

Coffee Break

15:30 – 15:45

Generative Models of Language Change

Gerhard Jäger (Department of Linguistics, University of Tübingen)

15:45 – 16:00

Title to be defined

Hendrik Lensch (Department of Computer Science, University of Tübingen)

 

16:00 – 16:15

Title to be defined

Detmar Meurers (Department of Linguistics, University of Tübingen)

16:15 – 16:30

Using Cognitive Psychology to Understand GPT-3

Eric Schulz (Max Planck Institute for Biological Cybernetics)

16:30 – 17:00

General Discussion & Concluding Remarks

17:00 –

Snacks and Refreshments

Philosophy of Science Meets Machine Learning (PhilML), October 20 - 22, 2022

Philosophy of Science Meets Machine Learning (PhilML)

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October 20 - 22, 2022

Lecture Hall, Max-Planck-Guest House, Max-Planck-Ring 6, 72076 Tübingen
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More information about the event as well as the program can be found here.

4th Annual Conference "Machine Learning in Science", July 12 + 13, 2022

4th Cluster Conference
"Machine Learning in Science" 2022
 

► Tuesday, July 12 | 9:00 am - 6:00 pm |

► Wednesday, July 13 | 9:00 am - 5:00 pm |

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Venue: Westspitze, Eisenbahnstraße 1, 72072 Tübingen


For all those who cannot attend the conference in person, we will stream the presentations on
Zoom

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PROGRAM

All times are provided in CEST, Central European Summer Time.

Program [PDF]

 

Tuesday, July 12, 2022

09:00

Ulrike von Luxburg, Philipp Berens
(Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen)

► Opening Remarks

09:15    

Gerard Pons-Moll
(Dept of Computer Science, University of Tübingen)

Virtual Humans - From Appearance to Behaviour

10:00  

Kira Rehfeld
(Dept of Geoscience, University of Tübingen)

Understanding Past, Present and Future Climate Evolution: Between Facts, Physics and Fiction

10:45

Coffee Break

11:15

Setareh Maghsudi
(Dept of Computer Science, University of Tübingen)

► Linear Combinatorial Semi-Bandit with Causally Related Rewards

12:00

Flora Jay
(The Interdisciplinary Computer Science Laboratory, Paris-Saclay University)

► KEYNOTE: Digging Historical Diversity Patterns out of Large-Scale Genomic Data using Exchangeable and Generative Neural Networks

talk online only

13:00

Lunch Break
14:15

Michèle Finck
(Faculty of Law, University of Tübingen)

► The EU's Legislative Agenda on AI

15:00

Frank Schreiber
(Dept of Physics, University of Tübingen)

► Machine Learning Applied to Scattering

15:45

Celestine Mendler-Dünner
(Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen)

► Social Dynamics in Learning and Decision-Making

16:30

Poster Session and Coffee

All Posters [PDF]
 

19:00

Conference Dinner at "Freistil" (registration required)


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Wednesday, July 13, 2022

09:00

Sergios Gatidis
(Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen)

Machine Learning for Science - and What About the Real World? Challenges for ML in Medicine

09:45

Michael Franke
(Dept of General and Computational Linguistics, University of Tübingen)

Probabilistic Models of Language Use

10:30

Charley Wu
(Cluster of Excellence "Machine Learning", University of Tübingen, and Tübingen AI Center)

The Trajectory of Human Development Resembles Stochastic Optimization in the Space of Learning Strategies

11:00

Coffee Break

11:15

Devis Tuia
(Environmental Computational Science and Earth Observation Laboratory, EPFL)

KEYNOTE: Machine Learning Supporting Ecology Supporting Machine Learning

talk online only

12:15

Richard Gao
(Cluster of Excellence "Machine Learning", University of Tübingen)

Simulation-based inference for discovering mechanistic models of neural population dynamics

12:45

Almut Sophia Köpke
(Cluster of Excellence "Machine Learning", University of Tübingen)

Multi-Modal Learning with Visual Information, Language, and Sound

13:15

Lunch Break

14:15

Cluster Network Project
Machine Learning in Education

14:45

Cluster Network Project
Modeling and Understanding Spatiotemporal Environmental Interactions

15:15

Cluster Network Project
Probabilistic Inference in Mechanistic Models

15:45

Cluster Network Project
Compositionality in Minds and Machines

16:15

Cluster Network Project
Uncovering the inner structure of medical images through generative modeling

16:45

Ulrike von Luxburg, Philipp Berens
(Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen)

Closing Remarks

Science and Innovation Days, June 29 - July 2, 2022

Science and Innovation Days

From June 29 to July 2, 2022, Tübingen's largest research institutions will open their doors and present their research to interested public. The Science and Innovation Days will take place all over the city, the program can be found  here. The events are in German.

The members of our cluster participate with the following contributions:
 

Wednesday, June 29, 2022
18:15

With Eric Schulz

Kick Off: Wissenschafft Zukunft - Wissenschaft und Gesellschaft im Dialog
More Info: https://uni-tuebingen.de/en/230849#c1565246

20:30

With Nicole Ludwig

► Künstliche Intelligenzen der Zukunft: Fakten und Fiktionen (Lesung)

More Info: https://uni-tuebingen.de/de/230849#c1565249

 
Friday, July 1, 2022

20:00

With Robert Bamler

Wieviel Science steckt in der Fiction? - Künstliche Intelligenz in Film und Forschung (Film und Diskussion)

More Info: https://uni-tuebingen.de/de/230858#c1583855

 
Saturday, July 2, 2022

09:30 - 10:30

With Tilman Gocht

Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort

More Info: https://uni-tuebingen.de/de/230852#c1583873

10:00 - 12:00

With Kerstin Rau, Thomas Gläßle

Wie gut versteht eine Maschine die Natur? Vorhersage von Bodentypen im Schönbuch

More Info: https://uni-tuebingen.de/de/230852#c1583873

10:00 - 12:00

With Georg Martius, Huanbo Sun

Wie Roboter fühlen können - ein sensitiver Roboterfinger mit Tastsinn

More Info: https://uni-tuebingen.de/de/230852#c1583873

10:00 - 10:30

and

11:30 - 12:00

For Kids

Bernhard Schölkopf: Warum sind Computer dumm?

More Info: https://uni-tuebingen.de/de/230852#c1583873

10:00 - 13:00

With Philipp Hennig

Info- und Feedbackstand: Was macht Cyber Valley?

More Info: https://uni-tuebingen.de/de/230852#c1583873

10:30 - 11:15

With Tilman Gocht

Hinter den Fassaden - Spaziergang über den KI-Forschungsstandort

More Info: https://uni-tuebingen.de/de/230852#c1583873

13:15

For Kids

Andreas Geiger: Kann künstliche Intelligenz kreativ sein? ( bereits ausgebucht)

Workshop for children as part of the Children's University Research Day

More Info: https://uni-tuebingen.de/de/2626#c547865

15:15

For Kids

Andreas Geiger: Kann künstliche Intelligenz kreativ sein?

Workshop for children as part of the Children's University Research Day. Registration until June 29 at kinderunispam prevention@uni-tuebingen.de

More Info: https://uni-tuebingen.de/de/2626#c548369

 

Workshop „AI and ML Research and Democracy”, April 2 - 4, 2022

PhD Workshop "Artificial Intelligence and Machine Learning Research and Democracy" followed by public panel discussion

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      April 2 - 4, 2022, at the University of Tübingen
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About the workshop:
As the use of Machine Learning algorithms and Artificial Intelligence pervades all areas of our lives and societies, it also affects fundamental pillars of democracy, such as public debate, political decision making and (dis)information through media. In our PhD workshop “Artificial Intelligence and Machine Learning research and democracy – an interdisciplinary perspective” we will provide a platform for young researchers from all relevant disciplines – from machine learning research to political science and every nuance in between – to engage in exchange of methods and current state of the art research. Our guiding question will be: How can we shape the co-development of AI/ ML research and democracy?

More information: ai-and-democracy-workshop.de

 

„KI gestaltet Demokratie – Demokratie gestaltet KI“ on April 4, starting 6:30 pm

At the end of the workshop, a public discussion on "AI and Democracy" will take place on April 4, starting 6:30 p.m., at the "Westspitze" in Tübingen.
Everyone interested is cordially invited to this event.
Registration via the following link: ai-and-democracy-workshop.de/podium
 

Workshop "Introduction to Machine Learning", March 30, 2022

Workshop on Introduction to Machine Learning

The Machine Learning ⇌ Science Colaboratory is glad to invite you to its in-person, hands-on Workshop on Introduction to Machine Learning.

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Wednesday, March 30, 2022, 9:00 to 13:00

The event will take place on site at Maria-von-Linden-str. 6, 72076 Tübingen, Lecture Hall (ground floor, room 00-28/A7).

Registration:
Apply for the workshop by filling out the  Registration Form
Please note that attendance is limited to 12 participants! You will hear from us approximately one week before the event.

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What? We’ll present first the fundamentals of ML with the aid of interactive demonstrations. Equipped with the necessary concepts, we will then jointly look at your specific research problems and data, and discuss how to design a ML project for them.

Who? Researchers from MSc student to PI level at the University of Tübingen who would like to know more about machine learning and how it can help with their research. Scholars from all disciplines are invited to join, no math or programming skills will be assumed.

How to prepare? Think about the characteristics of relevant datasets that you have access to, and be ready to discuss your use-case with the group. Bring your own laptop.

Covid regulations: 3G-rules apply (proof of vaccination or past infections or negative test) + FFP2 mask

 

For more information, please visit the Machine Learning ⇌ Science Colaboratory Website

Workshop "Philosophy of Science Meets Machine Learning", November 9 - 12, 2021

Workshop "Philosophy of Science Meets Machine Learning"
 

 The Workshop takes place in person in Tübingen
    on Nov.   9 - 10 in the Alte Aula (Münzgasse 30)
    on Nov. 11 - 12 in the Max Planck Institute for Intelligent Systems (Max-Planck-Ring 4)


The workshop is organised by the ‘Ethics and Philosophy Lab’ of the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen (Workshop Convenors: Thomas Grote, Thilo Hagendorff, Eric Raidl).

Registration: Space is limited. Guests please register via: rebigtimspam prevention@googlemail.com

Machine learning does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science, epistemology, research ethics and cognate fields such as sociology of science. The workshop will bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. The workshop`s central topics are:

  1. A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
  2. The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
  3. The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
  4. Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.

 Program

DAY 1: November 9 - Alte Aula

13:00 Registration & Coffee
13:50 Short Introduction
14:00 - 14:50 Gregory Wheeler - "Discounting Desirable Gambles"
15:00 - 15:40 Vlasta Sikimic - "Algorithmic grant review: benefits and limitations"
15:50 - 16:40 Emily Sullivan - "Stopping the Opacity Regress"
16:40 - 17:00 Coffee & Snacks
17:00 - 17:50 Bob Williamson - "(Un)stable facts, and (missing) chains of reference in machine learning"
  Evening activities / Dinner


DAY 2: November 10 - Alte Aula

9:00  - 9:50  Carlos Zednik - "The Explanatory Role of Explainable Artificial Intelligence"
10:00 - 10:40 Moritz Renftle et al. - "Evaluating the Effect of XAI on the Understanding of Machine Learning Models"
10:40 - 11:20 Timo Freiesleben - "To Explain and to Predict - Explanatory Machine Learning Models in Science"
11:20 - 11:40 Coffee & Snacks
11:40 - 12:30 Alex Broadbent - "Predictive Investigation and Deep Learning"
12:30 - 14:00 Extended lunch break
14:00 - 14:50

Jon Williamson - "Evidential Pluralism and Explainable AI"

15:00 - 15:40 Oliver Buchholz - "Towards a Means-End Account of XAI"
15:40 - 16:00 Break
16:00 - 16:40 Koray Karaca - "Inductive Risk and Values in Machine Learning"
16:40 - 17:30 Lena Kästner - "Grasping Psychopathology: On Complex and Computational Models"
  Informal discussion / Dinner


DAY 3: November 11 - MPI-IS

9:30 - 10:10      Benedikt Hoeltgen - "Causal Variable Selectrion Through Neural Networks"
10:10 - 10:50 Daniela Schuster - "Suspension of Judgment and Explainable AI"
10:50 - 11: 20 Coffee & Snacks
11:20 - 12:10 Anouk Barberousse - "Can Concept of Scientific Knowledge be Transformed by Machine Learning?"
12:10 - 14:00 Extended lunch break
14:00 - 14:40 Giorgio Gnecco et al. - "Simple Models in Complex Worlds: Occam's Razor and Statistcal Learning Theory" (Online)
14:40 - 15:20 Atoosa Kasirzadeh - "Kinds of Explanation in Machine Learning" (Online)
15:20 - 15:50 Coffee Break
15:50 - 16:30 Tim Räz - "Understanding Machine Learning for Empiricists"
16:30 - 17:20 Carina Prunkl - "Predictive Investigation and Deep Learning"
  Informal Discussion / Dinner


DAY 4: November 12 - MPI-IS

9:30 - 10:10     Mario Günther - "How to Attribute Beliefs to AI Systems?" (Online)
10:10 - 10:50 Dilectiss Liu - "Epistemic Opacity Does Not Undermine the Epistemic Justification of Machine Learning Models"
11:00 - 11:50 Kate Vredenburgh - "Against Rational Explanations"
11:50 - 12:20 Coffee Break
12:20 - 13:00 Roundtable

Talk by Claire Vernade - September 30, 2021


| Bandit learning with delays in Non-stationary environments |

Claire Vernade

DeepMind, London, UK
 

WHEN:      Thursday, 30. September 2021, 9:00 am
WHERE:     Hybrid-Event:
Claire will give her talk in the Lecture Hall, AI Building, Maria-von-Linden-Straße 6. Due to the current Covid restrictions, seats are limited. If you are a PI of our Cluster and want to join in person, please contact Elena Sizana to register. You need to be fully vaccinated, tested or recovered.
We invite everyone else to participate via Zoom: https://zoom.us/j/99906352769

 
ABSTRACT

We consider the problem of learning with delayed bandit feedback, meaning by trial and error, in changing environments. This problem is ubiquitous in many online recommender systems that aim at showing content, which is ultimately evaluated by long-term metrics like a purchase, or a watching time.  Mitigating the effects of delays in stationary environments is well-understood, but the problem becomes much more challenging when the environment changes. In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete. However, the arising issues can be addressed if relevant intermediate signals are available without delay, such that given those signals, the long-term behavior of the system is stationary. To model this situation, we introduce the problem of stochastic, non-stationary and delayed bandits with intermediate observations. We develop a computationally efficient algorithm based on UCRL, and prove sublinear regret guarantees for its performance.

Workshop "Simulation-based Inference for scientific discovery", 2021 September 20-22, ONLINE

Workshop "Simulation-based Inference for scientific discovery"

This workshop will be taking place online on Zoom.

The workshop is jointly organized by the ML⇌Science Colaboratory, the MLS Chair (Jakob Macke) and Helmholtz AI.

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Registration:
Apply for the workshop by filling out the Registration Form  Deadlin for application is Aug 31, 2021.

We can only select 20 participants for the workshop. To come up with that list and make a final decision, we will form a small selection board. We will inform successful applicants subsequently.

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You use simulation in physics, economics, archaeology or any other domain of your choice? You want to find the simulator's parameters that best fits the observations? Then simulation-based inference is something for you!

We teach a practical simulation-based inference workshop to help you understand recent machine learning techniques and apply them to your problem.

Apply to learn, have fun, and participate in a supportive and inclusive community. The workshop will combine lectures and practical hands-on sessions by experts in the field. We strive to provide a seamless computing environment for you to focus on the content rather than in import errors.

We plan to cover the following topics:

| Program

20. September

Using simulators for discovery, introduction to conditional density estimation.

  • a discussion of simulators
  • parameter inference
  • Bayesian Inference without Likelihoods
21. September

How does neural simulation-based inference work?

  • Bayes' Rule with neural density estimators
  • posterior estimation (SNPE)
  • likelihood estimation (SNLE and beyond)
22. September

Applying the sbi toolbox to your problem. Pitfalls, tricks and opportunities!

  • hands-on: sbi
  • hands-on: sbi and normalizing flows
  • hands-on: calibration or misspecification
  • bring your own data

 

3rd Annual Conference "Machine Learning in Science", July 12 + 13, 2021


3rd Cluster Conference
"Machine Learning in Science" 2021


► Monday,  July 12  | 2:00 pm - 6:00 pm |  Registration Link
                                     followed by an online theater at 19:30

► Tuesday, July 13  | 2:00 pm - 6:00 pm | Registration Link

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  The conference will take place virtually on Zoom.
The event is open to the public. Registration is required, separate for each day, see Registration Links above.

The theater on Monday evening is open to the public, further information here. The play will be performed in English and will be streamed live on Youtube. Afterwards, there will be a discussion with the actors and some researchers from our cluster. Registration is not required.
Links:
For the play https://tinyurl.com/SiliconWoman
For the discussion afterwards on Zoom https://zoom.us/j/91670801978

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PROGRAM

All times are provided in CEST, Central European Summer Time.

Program [PDF]
 

Monday, July 12, 2021

14:00 - 14:15  

Ulrike von Luxburg, Philipp Berens
(Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen)

► Opening remarks

14:15 - 14:45     

Robert Bamler
(Dept of Computer Science & Cluster of Excellence “Machine Learning”, University of Tübingen)

Maintaining Individual Agency in the Age of Big Data: Baby Steps

14:45 - 15:15      

Caterina De Bacco
(Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen)

Learning Reciprocity and Community Patterns in Networks

15:15 -15:30

BREAK

15:30 - 16:00

Konstantin Genin
(Dept of Computer Science & Cluster of Excellence “Machine Learning”, University of Tübingen)

► Clinical Equipoise and Causal Discovery

16:00 - 16:45

Spotlight Presentations

Innovation Fund Projects of the Cluster of Excellence "Machine Learning"

 

16:00 - 16:10     

David Künstle

Machine Learning Approaches for Psychophysics with Ordinal Comparisons

     

16:10 - 16:20 

Thomas Gläßle / Kerstin Rau

Interpretable Spatial Machine Learning for Environmental Modelling

 

16:20 - 16:30      

Daniel Weber

Human-Robot Interface with Eye-Tracking

    

16:30 - 16:40  

Valentyn Boreiko

Counterfactual Explanations of Decisions of Deep Neural Networks with Applications in Medical Diagnostics

16:45 - 17:00  

BREAK
17:00 - 17:30

Spotlight Presentations

Innovation Fund Projects of the Cluster of Excellence "Machine Learning"

 

17:00 - 17:10

Susanne Zabel

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

 

17:10 - 17:20

Lukas Fischer / Michael Nagel

Modelling Behavioral Responses to Emotional Cues in Sports - A Bayesian Approach

 

17:20 - 17:30

Francesco Carnazza

Understanding Quantum Effects in Neural Network Models through ML

17:30 - 18:00

Manfred Claassen
(Clinical Bioinformatics, Universitätsklinikum Tübingen)

► (Weakly) Supervised Learning of Disease Associated Cell States and Dynamics

18:00 - 19:30 BREAK
19:30 - 20:15

Theater

► Silicon Woman - the Singing Cyborg

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Tuesday, July 13, 2021

14:00 - 14:50            

Keynote Lecture

Neil Lawrence (The DeepMind Professor of Machine Learning, University of Cambridge)

Machine Learning and the Physical World

14:50 - 15:20      

Samira Samadi
(Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen)

Socially Fair k-Means Clustering

15:20 - 15:30     

BREAK

15:30 - 16:30

Spotlight Presentations
Innovation Fund Projects of the Cluster of Excellence "Machine Learning"

   

15:30 - 15:40 

Matthias Karlbauer

Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent CNN

 

15:40 - 15:50  

Pablo Sanchez Martin

Extracting Expertise from Tweets: Exploring the Boundary Conditions of Ambient Awareness

    

15:50 - 16:00

Zohreh Ghaderi / Hassan Shahmohammadi

Enhancing Machine Learning of Lexical Semantics with Image Mining

 

16:00 - 16:10    

Jonathan Fuhr

Applied Casual Inference in Social Sciences and Medicine

  

16:10 - 16:20

Jonas Ditz

Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response

  

16:20 - 17:30  

Alessandro Simon

Analytic Classical Density Functionals from an Equation Learning Network

16:30 - 16:45 BREAK
16:45 - 17:15

Peter Ochs
(Department of Mathematics, University of Tübingen)

Optimization for Machine Learning

17:15 - 17:45

Enkelejda Kasneci
(Dept of Computer Science & Cluster of Excellence “Machine Learning”, University of Tübingen)

► Machine Learning for intelligent Human-Computer Interaction

17:45 - 18:00

Ulrike von Luxburg / Philipp Berens
(Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen)

Closing Remarks

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

***************************************************************************************************************************************

Friday October 09, 2020 

 

 

 

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.

___________________________________________________________________________________________________________________________________________

PROGRAM

09:00    Welcome and Introduction: Cluster of Excellence – Machine Learning for Science
              Philipp Berens
09:10     ML Transfer Center and simulation based inference
              Álvaro Tejero-Cantero & Jakob Macke
09:40     Day-ahead optimization of production schedules for saving electrical energy costs
              Thomas Stüber & Michael Menth
10:00     Uncovering hidden structure in climate data
               Bedartha Goswami
10:20    Coffee Break
10:40    Learning spatiotemporal distributed generative graph neural networks
              Martin Butz
11:00    Modeling environmental processes in rivers
             Christiane Zarfl
11:20    Can plants learn? Coupling models and data in eco-evolutionary research
             Sara Tomiolo & Maximiliane Herberich (from Katja Tielbörger’s group)
11:40    Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by
             stacking machine learning models and rescanning covariate space

             Ruhollah Taghizadeh-Mehrjardi (from Thomas Scholten’s group)
12:00    The climate situation: Facts and Necessities
             Harald Baayen
12:05    Snack Break
12:30    ML and mobile robots in environmental science
             Andreas Zell
12:50    Turbulent transport of energy, momentum and matter by large data sets
              obtained from airborne probing of the lower atmosphere

             Jens Bange
13:20    Status of the CRC 1253 CAMPOS - Catchments as Reactors:
             Metabolism of Pollutants on the Landscape Scale

             Christiane Zarfl
13:40    Funding options in the ML Cluster of Excellence and beyond
             Tilman Gocht
14:00   End

_______________________________________________________________________________________________________________________________________________________________________________________________

  Aim of the day

Environmental science studies spatio-temporal dynamics of various processes and on different topics, including climate and weather, geology, hydrology, vegetation and agriculture, various forms of pollution (e.g. of organic pollutants), to name just a few. In all these cases, multiple, often interrelated data sources are available at varying degrees of spatial and temporal granularity. Moreover, human activities, such as river dam building, CO2 release, plantations, industry etc., strongly influence the unfolding dynamics. Critical principles – such as basic laws in physics – apply universally in such systems. Environmental science has strong expertise in modeling the underlying processes – typically by systems of partial or ordinary differential equations.

As a result, besides the expertise about the underlying processes, environmental science offers two types of data – real-world data as well as data from the respectively available models of the considered environmental system. This offers essentially the perfect basis for a meaningful, science-driven application of ML algorithms. On the one hand, the parameters of the differential equations may be optimized more effectively by means of state-of-the-art gradient-based approximation approaches from ML. On the other hand, the available models may be augmented or fully substituted by distributed spatio-temporal, generative neural network approaches, such as convolutional networks, graph networks, autoencoders, recurrent neural networks, and combinations thereof.

Seeing that models are available to pre-train and analyze potentially applicable ML architectures, expertise is available to tune these models to the actual underlying processes, and that real world data is available to further train and test the generalizability of these ML architectures, it is time that ML meets Environmental Science! The aim is to foster collaboration with a focus on two main potential strands. First, available models of differential equations and involved prior situation assumptions may be optimized by means of state-of-the-art ML techniques. Second, ML techniques and particularly distributed, generative artificial neural networks may be designed to infer the processes and structures that generate particular data patterns, thus enabling (i) the fast, efficient, and accurate simulation of environmental processes and (ii) the consideration of impacts of human actions, including the potential to derive optimal actions for steering the environmental system towards a desired (stable / homeostatic) direction.

2nd Annual Conference "Machine Learning in Science", July 21-23, 2020 -- ONLINE

2nd Cluster Conference
"Machine Learning in Science" 2020



► Tuesday, July 21  | 2:00 pm - 5:45 pm
► Wednesday, July 22  | 2:00 pm - 6:15 pm
► Thursday, July 23  | 2:00 pm - 6:30 pm

 

Change in program, Thursday July 23: Manfred Claassen's talk at 2 pm unfortunately needs to be cancelled. Phillip Berens kindly takes over the time slot at short notice with a talk on "Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance" contenteditable="false".

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The conference will take place virtually on Crowdcast with a live stream on Youtube.
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Registration
The conference is open to the public.
To participate in the discussions via chat you will have to register via Crowdcast for each conference day by clicking on the corresponding event on our Crowdcast profile - the participants' cameras and microphones will remain switched off. You need to enter your email address first and then your full name (first name and surname).
If you only want to follow the talks, you can watch them via Youtube. Here you  find the live streams of all conference talks.  

Each talk takes 30 minutes plus 15 minutes discussion, each spotlight presentation 5 minutes plus 5 minutes discussion.

Important: All times are given in CEST, Central European Summer Time.

Program [PDF]

Tuesday, July 21, 2020

14:00 - 14:15             

Ulrike von Luxburg, Philipp Berens
Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen

► Opening Remarks

Dr. Simone Schwanitz
Head of Section, State Ministry for Science, Research and Art, Baden-Württemberg

► 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
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

16:00 - 16:10      

David Künstle

Machine Learning Approaches for Psychophysics with Ordinal Comparisons

16:10 - 16:20      

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
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

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
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

14:50 - 15:00      

Jonas Ditz

Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response

15:00 - 15:10      

Susanne Zabel

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

15:10 - 15:20      

Paolo Mazza

Understanding Quantum Effects in Neural Network Models through Machine Learning

15:20 - 15:30      

Jonathan Fuhr

Applied Causal Inference in Social Sciences and Medicine

15:30 - 15:45      

BREAK

15:45 - 16:30      

Stefanie Jegelka (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology)

► Keynote Lecture: Representation and Learning in Graph Neural Networks

16:30 - 17:15      

Bedartha Goswami (Cluster of Excellence “Machine Learning”, University of Tübingen)

 Inferring Climate Variability from Patterns Hideen in Modern and Paleo Time Series Data

17:15 - 17:30      

BREAK

17:30 - 18:15      

Igor Lesanovsky (Department of Physics, University of Tübingen )

Neural Network Dynamics in Quantum Many-Body Systems

18:15 - 18:30       

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen

Closing Remarks

Symposium 'Machine Learning in Science', July 7-8, 2020 -- ONLINE

Symposium 'Machine Learning in Science'

on July 7 - 8, 2020

Zoom Videoconference

Each presentation takes 30 minutes and is followed by a discussion of 15 minutes.
The discussion is followed by a 45 minutes non-public session, which will be organised as break-out-group, hence ALL participants can stay in the online conference room for the entire duration of the symposium.

Registration:
Registration is required for Non-Cluster members only. Please send an email to Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de), the link to the Zoom conference can then be provided.

Important: Participants who do not provide their full names will be excluded from the video conference by the moderators.

Program [PDF]

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)
Machine Learning and Combinatorial Optimization

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

********************************************************************************************************************************

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

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" contenteditable="false"
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09:00 - 09:20    Introduction  &  Fairness in Machine Learning
                          Ulrike von Luxburg
09:20 - 09:40   On the Integration of Machine Learning into Healthcare
                          Thomas Grote
09:40 - 10:00   Explaining Neural Network Decisions with
                          Minimal Supervision         
                          Zeynep Akata

10:00 - 10:20   Explainability & Explanation
                          Eric Raidl

10:30 - 11:00 Coffee Break


  11:00 - 12:30  Session "Sociology"
--------------------------------------------------------

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.

Programm


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
and Some Estimation Problems

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)
Human-robot interface with eye-tracking and augmented reality to teach mobile robots about the real-world.

             University of Tübingen, Department of Computer Science

Valera I.1, Utz S.² (Cluster Innovation Fund Project)
Extracting expertise from tweets: Exploring the boundary conditions of ambient awareness).

             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)
Feature engineering for spatial modelling.

             University of Tübingen, 1Department of Geosciences, ²Department of Computer Science

Baayen H.1, Lensch H.² (Cluster Innovation Fund Project)
Enhancing Machine Learning of Lexical Semantics with Image Mining

            University of Tübingen, 1Department of Linguistics, ²Department of Computer Science

 

Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4
Automatic Data-driven Inference of Mechanistic Models

              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
Emotional cues and alcohol use: evidence from football.

               University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center

Kilian P.
Predicting math student college dropout with sparse information using approaches from statistical learning

              University of Tübingen, Methods Center

Klopotek M., Oettel M.
Variational autoencoders put up to the test in learning a statistical-mechanical model system

             University of Tübingen, Institut für Angewandte Physik

Lin SC, Oettel M.
Classical density functionals from machine learning

             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,
Schreiber F.1
Fast Scattering Data Analysis Using Machine Learning.
                1University of Tübingen, Institut für Angewandte Physik, Uni Tübingen, ² Bundesanstalt für Materialforschung
                 und -prüfung (BAM), Berlin

Sümer Ö.1,2, Kasneci E.1
Attention Flow: End-to-End Joint Attention Estimation

             University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and 
           Psychology (HIB)

Fuhl W., Kasneci G., Rosenstiel W., Kasneci E.
Training decision trees as replacement for convolution layers

               University of Tübingen, Department of Computer Science

Zadaianchuk A., Martius G.
Equation Learning for Extrapolation and Control

             Max Planck Institute for Intelligent Systems Tübingen

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

Program

09:45

Tropical circulation: Current challenges and potential for machine learning algorithms
Bedartha Goswami
-- Potsdam Institute for Climate Impact Research (PIK), Germany

10:30

High-throughput behavioral analysis for neural circuit understanding
Alexander Mathis
-- Department of Molecular and Cellular Biology, Harvard University, USA

11:15

Coffee Break

11:30

Reverse Engineering the Early Visual System with Artificial Neural Networks
Stéphane Deny
-- Department of Applied Physics at Stanford University, USA

12:15

Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research
Auriol Degbelo
-- Institute of Geography, University of Osnabrück, Deutschland

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

Program

08:30

Simplicity and Scientific Progress: A Topological Perspective
Konstantin Genin -- Department of Philosophy, University of Toronto, Canada

09:15

Learning Through Creativity
Caterina Moruzzi
-- Department of Philosophy, University of Nottingham, UK

10:00

Coffee break

10:20

Black-Boxes, Understanding, and Machine Learning
Emily Sullivan-Mumm
-- Ethics and Philosophy of Technology, Delft Data Science, The Netherlands

11:05

Working at the margins of machine learning – the ethics of labeling
Thilo Hagendorff
-- International Center for Ethics in the Sciences and Humanities,
University of Tübingen, Germany

11:50

Inductive Bias and Adversarial Data
Tom Sterkenburg -- LMU München, Munich Center for Mathematical Philosophy, Germany

12:35

Lunch break

13:30 - 14:30

Invited Talk

Co-Opt AI! Charting the emerging field of AI, ethics and social justice

Mona Sloane, Institute for Public Knowledge, New York University, USA
Abstract

17:10 - 17:55

Conference Room

ML from a DiscO viewpoint: Compressed Sensing, Dictionary Learning and beyond
(machine learning)

Andreas M. Tillmann -- Operations Research & Visual Computing Institute,
RWTH Aachen, Deutschland

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

Program

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
   Astrophysics, New York, USA

 

Monday March 25, 2019

08:30

Information Field Theory
   PD Dr. Torsten Enßlin -- MPI für Astrophysik, Garching

09:30

Active machine learning for automating scientific discovery
   Prof. Dr. Roman Garnett -- Washington University in St. Louis, USA

10:30

Coffee break

11:00

Bayesian optimisation: nano-machine-learning
   Assoc. Prof. Dr. Michael Osborne -- University of Oxford, UK

12:00

Robust and Scalable Learning with Graphs
   Prof. Dr. Stephan Günnemann  -- TU München

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
   Dr. Isabel Valera -- MPI for Intelligent Systems, Tübingen

09:30

Algorithms of Vision: From Brains to Machines and Back
   Dr. Alexander Ecker -- Universität Tübingen

10:30

Coffee break

11:00

From Paired to Unpaired Image-to-Image Translation and Beyond
   Dr. Radu Timofte -- ETH Zürich, Schweiz

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
=========
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

Download program