Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J. Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (misc) Accepted

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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)

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

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Bonse, M. J., Gebhard, T. D., Dannert, F. A., Absil, O., Cantalloube, F., Christiaens, V., Cugno, G., Garvin, E. O., Hayoz, J., Kasper, M., Matthews, E., Schölkopf, B., Quanz, S. P. Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011 2024 (misc) Submitted

arXiv [BibTex]

Buchholz, S., Kübler, J. M., Schölkopf, B. Multi-Armed Bandits and Quantum Channel Oracles 2024 (misc)

arXiv [BibTex]

Rajendran, G., Buchholz, S., Aragam, B., Schölkopf, B., Ravikumar, P. Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models 2024 (misc)

arXiv [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)

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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]

Bottou, L., Schölkopf, B. Borges und die Künstliche Intelligenz 2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (misc)

PDF [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)

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

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Wang, H., Jin, Z., Cao, J., Fung, G. P. C., Wong, K. Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning 2022 (misc)

arXiv [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)

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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)

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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]

Prabhoo, S., Bauer, S., Schwab, P. NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments 2021 (misc)

arXiv [BibTex]

Abdulsamad, H., Dorau, T., Belousov, B., Zhu, J., Peters, J. Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions 2021 (misc)

arXiv Project Page [BibTex]

Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S. Pulling back information geometry 2021 (misc)

arXiv [BibTex]

Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., Srinivasa, S. S., Garg, A., Buchholz, A., Stark, S., Steinbrenner, T., Akpo, J., Joshi, S., Agrawal, V., Schölkopf, B. A Robot Cluster for Reproducible Research in Dexterous Manipulation 2021, *equal contribution (misc)

arXiv [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]

Panda, S., Shen, C., Perry, R., Zorn, J., Lutz, A., Priebe, C. E., Vogelstein, J. T. Nonpar MANOVA via Independence Testing 2021 (misc)

arXiv [BibTex]

Shao, K., Villegas, J. F. R., Logothetis, N. K., Besserve, M. A model of Ponto-Geniculo-Occipital waves supports bidirectional control of cortical plasticity across sleep-stages 2021 (misc) In preparation

bioRxiv DOI [BibTex]

Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G. On the Impact of Stable Ranks in Deep Nets 2021 (misc)

arXiv [BibTex]

Scientific Report 2016 - 2021 2021 (mpi_year_book)

Scientific Report 2016 - 2021 [BibTex]

Perry, R., Tomita, T. M., Mehta, R., Arroyo, J., Patsolic, J., Falk, B., Vogelstein, J. T. Manifold forests: closing the gap on neural networks 2021 (misc)

arXiv [BibTex]

Perry, R., Mehta, R., Guo, R., Yezerets, E., Arroyo, J., Powell, M., Helm, H., Shen, C., Vogelstein, J. T. Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities 2021 (misc)

arXiv [BibTex]

Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A. Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger 2021 (misc)

arXiv [BibTex]