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

Project Page [BibTex]

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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Rahaman, N., Weiss, M., Wüthrich, M., Bengio, Y., Li, E., Pal, C., Schölkopf, B. Language Models Can Reduce Asymmetry in Information Markets arXiv:2403.14443, March 2024, Published as: Redesigning Information Markets in the Era of Language Models, Conference on Language Modeling (COLM) (techreport)

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Ortu, F. Interpreting How Large Language Models Handle Facts and Counterfactuals through Mechanistic Interpretability University of Trieste, Italy, March 2024 (mastersthesis)

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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]

Sakenyte, U. Denoising Representation Learning for Causal Discovery Université de Genèva, Switzerland, December 2023, external supervision (mastersthesis)

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Kofler, A. Efficient Sampling from Differentiable Matrix Elements Technical University of Munich, Germany, September 2023 (mastersthesis)

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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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Spieler, A. M. Intrinsic complexity and mechanisms of expressivity of cortical neurons University of Tübingen, Germany, March 2023 (mastersthesis)

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Kladny, K. CausalEffect Estimation by Combining Observational and Interventional Data ETH Zurich, Switzerland, February 2023 (mastersthesis)

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Qui, Z. Towards Generative Machine Teaching Technical University of Munich, Germany, February 2023 (mastersthesis)

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Schneider, F. ArchiSound: Audio Generation with Diffusion ETH Zurich, Switzerland, January 2023, external supervision (mastersthesis)

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Dittrich, A. Generation and Quantification of Spin in Robot Table Tennis University of Stuttgart, Germany, January 2023 (mastersthesis)

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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]

Berenz, V., Widmaier, F., Guist, S., Schölkopf, B., Büchler, D. Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80 Robot Software Architectures Workshop (RSA) 2023, ICRA, 2023 (techreport)

arxiv poster link (url) [BibTex]

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

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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)

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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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Liang, W. Investigating Independent Mechanisms in Neural Networks Université Paris-Saclay, France, October 2022 (mastersthesis)

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Keidar, D. Modeling subgroup differences in fMRI data: disentangling subgroup-specific responses from shared ones ETH Zurich, Switzerland, October 2022 (mastersthesis)

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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)

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Feil, M. Multi-Target Multi-Object Manipulation using Relational Deep Reinforcement Learning Technnical University Munich, Germany, September 2022 (mastersthesis)

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Sliwa, J. Independent Mechanism Analysis for High Dimensions University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (mastersthesis)

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

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Dominguez-Olmedo, R. On the Adversarial Robustness of Causal Algorithmic Recourse University of Tübingen, Germany, August 2022 (mastersthesis)

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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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Ghosh, S. Independent Mechanism Analysis in High-Dimensional Observation Spaces ETH Zurich, Switzerland, June 2022 (mastersthesis)

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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)

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]

Scherrer, N. Learning Neural Causal Models with Active Interventions ETH Zurich, Switzerland, November 2021 (mastersthesis)

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Bing, S. HealthGen: Conditional Generation of Realistic Medical Time Series with Informative Missingness ETH Zurich, Switzerland, October 2021 (mastersthesis)

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Lanzillotta, G. Study of the Interventional Consistency of Autoencoders ETH Zurich, Switzerland, October 2021 (mastersthesis)

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Mambelli, D. Training with Few to Manipulate Many. On OOD generalization in relational reinforcement learning ETH Zurich, Switzerland, October 2021 (mastersthesis)

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