2024
von Kügelgen, J.
Identifiable Causal Representation Learning
University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (phdthesis)
2023
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)
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)
Schölkopf, B.
Causality, causal digital twins, and their applications
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382), (Editors: Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica), September 2022 (talk)
Tabibian, B.
Methods for Minimizing the Spread of Misinformation on the Web
University of Tübingen, Germany, September 2022 (phdthesis)
Huang, B.
Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence
Carnegie Mellon University, Pittsburgh, USA, July 2022 (phdthesis)
Ialongo, A.
Variational Inference in Dynamical Systems
University of Cambridge, UK, Cambridge, February 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)
2021
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)
Belousov, B., H., A., Klink, P., Parisi, S., Peters, J.
Reinforcement Learning Algorithms: Analysis and Applications
883, Studies in Computational Intelligence, Springer International Publishing, 2021 (book)
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)
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)
2019
Büchler, D.
Robot Learning for Muscular Systems
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Safavi, S., Logothetis, N., Besserve, M.
Multivariate coupling estimation between continuous signals and point processes
Neural Information Processing Systems 2019 - Workshop on Learning with Temporal Point Processes, December 2019 (talk)
Gomez-Gonzalez, S.
Real Time Probabilistic Models for Robot Trajectories
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Lutz, P.
Automatic Segmentation and Labelling for Robot Table Tennis Time Series
Technical University Darmstadt, Germany, August 2019 (thesis)
Rojas-Carulla, M.
Learning Transferable Representations
University of Cambridge, UK, February 2019 (phdthesis)
Gu, S.
Sample-efficient deep reinforcement learning for continuous control
University of Cambridge, UK, 2019 (phdthesis)
Weichwald, S.
Pragmatism and Variable Transformations in Causal Modelling
ETH Zurich, 2019 (phdthesis)
Ścibior*, A.
Formally justified and modular Bayesian inference for probabilistic programs
University of Cambridge, UK, 2019 (phdthesis)
Katiyar, P.
Quantification of tumor heterogeneity using PET/MRI and machine learning
Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)
Bauer, M.
Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
University of Cambridge, UK, 2019 (phdthesis)
2018
Koc, O.
Optimal Trajectory Generation and Learning Control for Robot Table Tennis
Technical University Darmstadt, Germany, 2018 (phdthesis)
Simon-Gabriel, C. J.
Distribution-Dissimilarities in Machine Learning
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
Mahsereci, M.
Probabilistic Approaches to Stochastic Optimization
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
Schober, M.
Probabilistic Ordinary Differential Equation Solvers — Theory and Applications
Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
Jayaram, V.
A machine learning approach to taking EEG-based computer interfaces out of the lab
Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)
2017
Peters, J., Janzing, D., Schölkopf, B.
Elements of Causal Inference - Foundations and Learning Algorithms
Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)
Klenske, E. D.
Nonparametric Disturbance Correction and Nonlinear Dual Control
(24098), ETH Zurich, 2017 (phdthesis)
Fomina, T.
Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis
Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)
Geiger, P.
Causal models for decision making via integrative inference
University of Stuttgart, Germany, 2017 (phdthesis)
2016
Castaneda, S., Katiyar, P., Russo, F., Calaminus, C., Disselhorst, J. A., Ziemann, U., Kohlhofer, U., Quintanilla-Martinez, L., Poli, S., Pichler, B. J.
Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke
World Molecular Imaging Conference, 2016 (talk)
Kiefel, M.
Tractable Structured Prediction using the Permutohedral Lattice
ETH Zurich, 2016 (phdthesis)
Köhler, R.
Advances in computational imaging: Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields
Eberhard Karls Universität Tübingen, Germany, 2016 (phdthesis)
Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J., Disselhorst, J. A.
Multi-view learning on multiparametric PET/MRI quantifies intratumoral heterogeneity and determines therapy efficacy
World Molecular Imaging Conference, 2016 (talk)
2015
Grimm, Dominik
easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies
Eberhard Karls Universität Tübingen, November 2015 (phdthesis)
Sgouritsa, E.
Causal Discovery Beyond Conditional Independences
Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)