Immer, A. Advances in Probabilistic Methods for Deep Learning ETH Zurich, Switzerland, September 2024, CLS PhD Program (phdthesis)

[BibTex]

Park, J. A Measure-Theoretic Axiomatisation of Causality and Kernel Regression University of Tübingen, Germany, July 2024 (phdthesis)

[BibTex]

Sajjadi, S. M. M. Enhancement and Evaluation of Deep Generative Networks with Applications in Super-Resolution and Image Generation University of Tübingen, Germany, July 2024 (phdthesis)

[BibTex]

Stimper, V. Advancing Normalising Flows to Model Boltzmann Distributions University of Cambridge, UK, Cambridge, June 2024, (Cambridge-Tübingen-Fellowship-Program) (phdthesis)

[BibTex]

Besserve, M., Safavi, S., Schölkopf, B., Logothetis, N. LFP transient events in macaque subcortical areas reveal network coordination across scales and structures: a simultaneous fMRI-electrophysiology study Computational and Systems Neuroscience Meeting (COSYNE), March 2024 (poster)

link (url) [BibTex]

Shao, K., Xu, Y., Logothetis, N., Shen, Z., Besserve, M. Koopman Spectral Analysis Uncovers the Temporal Structure of Spontaneous Neural Events Computational and Systems Neuroscience Meeting (COSYNE), March 2024 (poster)

link (url) [BibTex]

von Kügelgen, J. Identifiable Causal Representation Learning University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

Jenny, D. Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures ETH Zurich, Switzerland, November 2023, external supervision (thesis)

[BibTex]

Karimi, A. Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness ETH Zurich, Switzerland, July 2023 (phdthesis)

[BibTex]

Kübler, J. M. Learning and Testing Powerful Hypotheses University of Tübingen, Germany, July 2023 (phdthesis)

[BibTex]

Gresele, L. Learning Identifiable Representations: Independent Influences and Multiple Views University of Tübingen, Germany, June 2023 (phdthesis)

[BibTex]

Paulus, M. Learning with and for discrete optimization ETH Zurich, Switzerland, May 2023, CLS PhD Program (phdthesis)

[BibTex]

Neitz, A. Towards learning mechanistic models at the right level of abstraction University of Tübingen, Germany, November 2022 (phdthesis)

[BibTex]

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)

[BibTex]

Wenk, P. Learning Time-Continuous Dynamics Models with Gaussian-Process-Based Gradient Matching ETH Zurich, Switzerland, October 2022, CLS PhD Program (phdthesis)

[BibTex]

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)

link (url) DOI [BibTex]

Tabibian, B. Methods for Minimizing the Spread of Misinformation on the Web University of Tübingen, Germany, September 2022 (phdthesis)

[BibTex]

Huang, B. Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence Carnegie Mellon University, Pittsburgh, USA, July 2022 (phdthesis)

[BibTex]

Huang, B. Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence Carnegie Mellon University, July 2022, external supervision (phdthesis)

[BibTex]

Ialongo, A. Variational Inference in Dynamical Systems University of Cambridge, UK, Cambridge, February 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

Mehrjou, A. Dynamics of Learning and Learning of Dynamics ETH Zürich, Zürich, October 2021 (phdthesis)

DOI [BibTex]

Hohmann, M. A Large Scale Brain-Computer Interface for Patients with Neurological Diseases University of Tübingen, Germany, September 2021 (phdthesis)

[BibTex]

Parascandolo, G. Deep Learning Beyond The Training Distribution ETH Zürich, Switzerland, Zürich, September 2021, (CLS Fellowship Program) (phdthesis)

DOI [BibTex]

Raj, A. Optimization Algorithms for Machine Learning University of Tübingen, Germany, June 2021 (phdthesis)

[BibTex]

Meding, K. Causal Inference in Vision Eberhard Karls Universität Tübingen, Tübingen, June 2021 (phdthesis)

[BibTex]

Bradshaw, J. Machine Learning Methods for Modeling Synthesizable Molecules University of Cambridge, UK, Cambridge, April 2021, (Cambridge-Tübingen-Fellowship) (phdthesis)

DOI [BibTex]

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)

DOI [BibTex]

Scientific Report 2016 - 2021 2021 (mpi_year_book)

Scientific Report 2016 - 2021 [BibTex]

Mastakouri, A. Causal Feature Selection in Neuroscience University of Tübingen, Germany, December 2020 (phdthesis)

link (url) [BibTex]

Locatello, F. Enforcing and Discovering Structure in Machine Learning ETH Zurich, Switzerland, November 2020, (CLS Fellowship Program) (phdthesis)

[BibTex]

Bécigneul, G. On the Geometry of Data Representations ETH Zurich, Switzerland, September 2020, (CLS Fellowship Program) (phdthesis)

[BibTex]

Kilbertus, N. Beyond traditional assumptions in fair machine learning University of Cambridge, UK, September 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

Rubenstein, P. Advances in Latent Variable and Causal Models University of Cambridge, UK, July 2020, (Cambridge-Tuebingen-Fellowship) (phdthesis)

[BibTex]

Wieschollek, P. Learning from Multi-Frame Data University of Tübingen, Germany, July 2020 (phdthesis)

[BibTex]

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)

[BibTex]

Büchler, D. Robot Learning for Muscular Systems Technical University Darmstadt, Germany, December 2019 (phdthesis)

link (url) [BibTex]

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)

Talk video link (url) [BibTex]

Gomez-Gonzalez, S. Real Time Probabilistic Models for Robot Trajectories Technical University Darmstadt, Germany, December 2019 (phdthesis)

[BibTex]

Meding, K., Schölkopf, B., Wichmann, F. A. Perception of temporal dependencies in autoregressive motion Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (poster)

link (url) [BibTex]

Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A. Phenomenal Causality and Sensory Realism Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (poster)

link (url) [BibTex]

Lutz, P. Automatic Segmentation and Labelling for Robot Table Tennis Time Series Technical University Darmstadt, Germany, August 2019 (thesis)

[BibTex]

Shao, K., Logothetis, N., Besserve, M. Neural mass modeling of the Ponto-Geniculo-Occipital wave and its neuromodulation 28th Annual Computational Neuroscience Meeting (CNS*2019), July 2019 (poster)

DOI [BibTex]

Rojas-Carulla, M. Learning Transferable Representations University of Cambridge, UK, February 2019 (phdthesis)

[BibTex]

Gu, S. Sample-efficient deep reinforcement learning for continuous control University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Weichwald, S. Pragmatism and Variable Transformations in Causal Modelling ETH Zurich, 2019 (phdthesis)

[BibTex]

Scientific Report 2016 - 2018 2019 (mpi_year_book)

Scientific Report 2016 - 2018 [BibTex]

Ścibior*, A. Formally justified and modular Bayesian inference for probabilistic programs University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Katiyar, P. Quantification of tumor heterogeneity using PET/MRI and machine learning Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

Bauer, M. Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Goris, R., Henaff, O., Meding, K. Representation of sensory uncertainty in macaque visual cortex Computational and Systems Neuroscience (COSYNE) 2018, March 2018 (poster)

[BibTex]