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

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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)

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

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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)

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

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

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

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

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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)

DOI [BibTex]

Biester, L., Demszky, D., Jin, Z., Sachan, M., Tetreault, J., Wilson, S., Xiao, L., Zhao, J. Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI) Association for Computational Linguistics, December 2022 (proceedings)

link (url) [BibTex]

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

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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)

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

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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)

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

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

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Schölkopf, B., Uhler, C., Zhang, K. Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022) 177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

link (url) [BibTex]

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

[BibTex]

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)

arXiv DOI [BibTex]

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)

DOI [BibTex]

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)

DOI [BibTex]

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)

arXiv DOI [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)

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

DOI [BibTex]

Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C. Proceedings of the 1st Workshop on NLP for Positive Impact Association for Computational Linguistics, August 2021 (proceedings)

link (url) [BibTex]

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

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

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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)

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

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

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

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

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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)

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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)

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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]

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)

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

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