2024
von Kügelgen, J.
Identifiable Causal Representation Learning
University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (phdthesis)
2023
Kofler, A.
Efficient Sampling from Differentiable Matrix Elements
Technical University of Munich, Germany, September 2023 (mastersthesis)
Karimi, A.
Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness
ETH Zurich, Switzerland, July 2023 (phdthesis)
Kübler, J. M.
Learning and Testing Powerful Hypotheses
University of Tübingen, Germany, July 2023 (phdthesis)
Gresele, L.
Learning Identifiable Representations: Independent Influences and Multiple Views
University of Tübingen, Germany, June 2023 (phdthesis)
Paulus, M.
Learning with and for discrete optimization
(ETH Zurich, Switzerland), May 2023, CLS PhD Program (phdthesis)
Spieler, A. M.
Intrinsic complexity and mechanisms of expressivity of cortical neurons
University of Tübingen, Germany, March 2023 (mastersthesis)
Qui, Z.
Towards Generative Machine Teaching
Technical University of Munich, Germany, February 2023 (mastersthesis)
Dittrich, A.
Generation and Quantification of Spin in Robot Table Tennis
University of Stuttgart, Germany, January 2023 (mastersthesis)
Jin, Z., Mihalcea, R.
Natural Language Processing for Policymaking
In Handbook of Computational Social Science for Policy, pages: 141-162, 7, (Editors: Bertoni, E. and Fontana, M. and Gabrielli, L. and Signorelli, S. and Vespe, M.), Springer International Publishing, 2023 (inbook)
2022
Neitz, A.
Towards learning mechanistic models at the right level of abstraction
University of Tübingen, Germany, November 2022 (phdthesis)
Lu, C.
Learning Causal Representations for Generalization and Adaptation in Supervised, Imitation, and Reinforcement Learning
University of Cambridge, UK, Cambridge, October 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)
Liang, W.
Investigating Independent Mechanisms in Neural Networks
Université Paris-Saclay, France, October 2022 (mastersthesis)
Keidar, D.
Modeling subgroup differences in fMRI data: disentangling subgroup-specific responses from shared ones
ETH Zurich, Switzerland, October 2022 (mastersthesis)
Feil, M.
Multi-Target Multi-Object Manipulation using Relational Deep Reinforcement Learning
Technnical University Munich, Germany, September 2022 (mastersthesis)
Sliwa, J.
Independent Mechanism Analysis for High Dimensions
University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (mastersthesis)
Tabibian, B.
Methods for Minimizing the Spread of Misinformation on the Web
University of Tübingen, Germany, September 2022 (phdthesis)
Dominguez-Olmedo, R.
On the Adversarial Robustness of Causal Algorithmic Recourse
University of Tübingen, Germany, August 2022 (mastersthesis)
Huang, B.
Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence
Carnegie Mellon University, Pittsburgh, USA, July 2022 (phdthesis)
Ghosh, S.
Independent Mechanism Analysis in High-Dimensional Observation Spaces
ETH Zurich, Switzerland, June 2022 (mastersthesis)
Ialongo, A.
Variational Inference in Dynamical Systems
University of Cambridge, UK, Cambridge, February 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)
Peters, J., Bauer, S., Pfister, N.
Causal Models for Dynamical Systems
In Probabilistic and Causal Inference: The Works of Judea Pearl, pages: 671-690, 1, Association for Computing Machinery, 2022 (inbook)
Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I.
Towards Causal Algorithmic Recourse
In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pages: 139-166, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (inbook)
Schölkopf, B.
Causality for Machine Learning
In Probabilistic and Causal Inference: The Works of Judea Pearl, pages: 765-804, 1, Association for Computing Machinery, New York, NY, USA, 2022 (inbook)
Salewski, L., Koepke, A. S., Lensch, H. P. A., Akata, Z.
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pages: 69-88, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (inbook)
2021
Scherrer, N.
Learning Neural Causal Models with Active Interventions
ETH Zurich, Switzerland, November 2021 (mastersthesis)
Bing, S.
HealthGen: Conditional Generation of Realistic Medical Time Series with Informative Missingness
ETH Zurich, Switzerland, October 2021 (mastersthesis)
Lanzillotta, G.
Study of the Interventional Consistency of Autoencoders
ETH Zurich, Switzerland, October 2021 (mastersthesis)
Mambelli, D.
Training with Few to Manipulate Many. On OOD generalization in relational reinforcement learning
ETH Zurich, Switzerland, October 2021 (mastersthesis)
Mehrjou, A.
Dynamics of Learning and Learning of Dynamics
ETH Zürich, Zürich, October 2021 (phdthesis)
Hohmann, M.
A Large Scale Brain-Computer Interface for Patients with Neurological Diseases
University of Tübingen, Germany, September 2021 (phdthesis)
Parascandolo, G.
Deep Learning Beyond The Training Distribution
ETH Zürich, Switzerland, Zürich, September 2021, (CLS Fellowship Program) (phdthesis)
Raj, A.
Optimization Algorithms for Machine Learning
University of Tübingen, Germany, June 2021 (phdthesis)
Meding, K.
Causal Inference in Vision
Eberhard Karls Universität Tübingen, Tübingen, June 2021 (phdthesis)
Bradshaw, J.
Machine Learning Methods for Modeling Synthesizable Molecules
University of Cambridge, UK, Cambridge, April 2021, (Cambridge-Tübingen-Fellowship) (phdthesis)
2020
Mastakouri, A.
Causal Feature Selection in Neuroscience
University of Tübingen, Germany, December 2020 (phdthesis)
Locatello, F.
Enforcing and Discovering Structure in Machine Learning
ETH Zurich, Switzerland, November 2020, (CLS Fellowship Program) (phdthesis)
Ahmed, O.
A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
ETH Zurich, Switzerland, October 2020 (mastersthesis)
DuMont Schütte, A.
A Comprehensive Benchmark Evaluation of Synthetic Data Generation for Biomedical Imaging
ETH Zurich, Switzerland, October 2020 (mastersthesis)
Bécigneul, G.
On the Geometry of Data Representations
ETH Zurich, Switzerland, September 2020, (CLS Fellowship Program) (phdthesis)
Kilbertus, N.
Beyond traditional assumptions in fair machine learning
University of Cambridge, UK, September 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)
Rubenstein, P.
Advances in Latent Variable and Causal Models
University of Cambridge, UK, July 2020, (Cambridge-Tuebingen-Fellowship) (phdthesis)
Wieschollek, P.
Learning from Multi-Frame Data
University of Tübingen, Germany, July 2020 (phdthesis)
Balog, M.
Converting to Optimization in Machine Learning: Perturb-and-MAP, Differential Privacy, and Program Synthesis
University of Cambridge, UK, July 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)
Cacioppo, A.
Deep learning for the parameter estimation of tight-binding Hamiltonians
University of Roma, La Sapienza, Italy, May 2020 (mastersthesis)
Zecevic, M.
Learning Algorithms, Invariances, and the Real World
Technical University of Darmstadt, Germany, April 2020 (mastersthesis)
2019
Büchler, D.
Robot Learning for Muscular Systems
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Gomez-Gonzalez, S.
Real Time Probabilistic Models for Robot Trajectories
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Stimper, V.
Inferring the Band Structure from Band Mapping Data through Machine Learning
Technical University of Munich, September 2019 (mastersthesis)
Dietz, B.
Learning to Diagnose Diabetes from Magnetic Resonance Tomography
ETH Zurich, Switzerland, August 2019 (mastersthesis)