Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

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

[BibTex]

LFP transient events in macaque subcortical areas reveal network coordination across scales and structures: a simultaneous fMRI-electrophysiology study

Besserve, M., Safavi, S., Schölkopf, B., Logothetis, N.

Computational and Systems Neuroscience Meeting (COSYNE), March 2024 (poster)

link (url) [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]

Language Models Can Reduce Asymmetry in Information Markets

Rahaman, N., Weiss, M., Wüthrich, M., Bengio, Y., Li, E., Pal, C., Schölkopf, B.

arXiv:2403.14443, March 2024, Published as: Redesigning Information Markets in the Era of Language Models, Conference on Language Modeling (COLM) (techreport)

Abstract

This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.

link (url) [BibTex]

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)

link (url) [BibTex]

Koopman Spectral Analysis Uncovers the Temporal Structure of Spontaneous Neural Events

Shao, K., Xu, Y., Logothetis, N., Shen, Z., Besserve, M.

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]

Interpreting How Large Language Models Handle Facts and Counterfactuals through Mechanistic Interpretability

Ortu, F.

University of Trieste, Italy, March 2024 (mastersthesis)

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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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Identifiable Causal Representation Learning

von Kügelgen, J.

University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (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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2023

Denoising Representation Learning for Causal Discovery

Sakenyte, U.

Université de Genèva, Switzerland, December 2023, external supervision (mastersthesis)

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2023

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

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Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures

Jenny, D.

ETH Zurich, Switzerland, November 2023, external supervision (thesis)

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

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Efficient Sampling from Differentiable Matrix Elements

Kofler, A.

Technical University of Munich, Germany, September 2023 (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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Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness

Karimi, A.

ETH Zurich, Switzerland, July 2023 (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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Learning and Testing Powerful Hypotheses

Kübler, J. M.

University of Tübingen, Germany, 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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Learning Identifiable Representations: Independent Influences and Multiple Views

Gresele, L.

University of Tübingen, Germany, June 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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Learning with and for discrete optimization

Paulus, M.

ETH Zurich, Switzerland, May 2023, CLS PhD Program (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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Intrinsic complexity and mechanisms of expressivity of cortical neurons

Spieler, A. M.

University of Tübingen, Germany, March 2023 (mastersthesis)

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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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CausalEffect Estimation by Combining Observational and Interventional Data

Kladny, K.

ETH Zurich, Switzerland, February 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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Towards Generative Machine Teaching

Qui, Z.

Technical University of Munich, Germany, 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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ArchiSound: Audio Generation with Diffusion

Schneider, F.

ETH Zurich, Switzerland, January 2023, external supervision (mastersthesis)

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

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Generation and Quantification of Spin in Robot Table Tennis

Dittrich, A.

University of Stuttgart, Germany, January 2023 (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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Natural Language Processing for Policymaking

Jin, Z., Mihalcea, R.

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]

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]

Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80

Berenz, V., Widmaier, F., Guist, S., Schölkopf, B., Büchler, D.

Robot Software Architectures Workshop (RSA) 2023, ICRA, 2023 (techreport)

Abstract

Robotic applications require the integration of various modalities, encompassing perception, control of real robots and possibly the control of simulated environments. While the state-of-the-art robotic software solutions such as ROS 2 provide most of the required features, flexible synchronization between algorithms, data streams and control loops can be tedious. o80 is a versatile C++ framework for robotics which provides a shared memory model and a command framework for real-time critical systems. It enables expert users to set up complex robotic systems and generate Python bindings for scientists. o80's unique feature is its flexible synchronization between processes, including the traditional blocking commands and the novel ``bursting mode'', which allows user code to control the execution of the lower process control loop. This makes it particularly useful for setups that mix real and simulated environments.

arxiv poster link (url) [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]

2022

Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

Biester, L., Demszky, D., Jin, Z., Sachan, M., Tetreault, J., Wilson, S., Xiao, L., Zhao, J.

Association for Computational Linguistics, December 2022 (proceedings)

link (url) [BibTex]

2022

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]

Towards learning mechanistic models at the right level of abstraction

Neitz, A.

University of Tübingen, Germany, November 2022 (phdthesis)

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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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Learning Causal Representations for Generalization and Adaptation in Supervised, Imitation, and Reinforcement Learning

Lu, C.

University of Cambridge, UK, Cambridge, October 2022, (Cambridge-Tübingen-Fellowship) (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)

[BibTex]

Investigating Independent Mechanisms in Neural Networks

Liang, W.

Université Paris-Saclay, France, October 2022 (mastersthesis)

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

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Modeling subgroup differences in fMRI data: disentangling subgroup-specific responses from shared ones

Keidar, D.

ETH Zurich, Switzerland, 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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Learning Time-Continuous Dynamics Models with Gaussian-Process-Based Gradient Matching

Wenk, P.

ETH Zurich, Switzerland, October 2022, CLS PhD Program (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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Causality, causal digital twins, and their applications

Schölkopf, B.

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]

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]

Multi-Target Multi-Object Manipulation using Relational Deep Reinforcement Learning

Feil, M.

Technnical University Munich, Germany, September 2022 (mastersthesis)

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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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Independent Mechanism Analysis for High Dimensions

Sliwa, J.

University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (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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Methods for Minimizing the Spread of Misinformation on the Web

Tabibian, B.

University of Tübingen, Germany, September 2022 (phdthesis)

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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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On the Adversarial Robustness of Causal Algorithmic Recourse

Dominguez-Olmedo, R.

University of Tübingen, Germany, August 2022 (mastersthesis)

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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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Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence

Huang, B.

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, Pittsburgh, USA, July 2022 (phdthesis)

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Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence

Huang, B.

Carnegie Mellon University, July 2022, external supervision (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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Independent Mechanism Analysis in High-Dimensional Observation Spaces

Ghosh, S.

ETH Zurich, Switzerland, June 2022 (mastersthesis)

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

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Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022)

Schölkopf, B., Uhler, C., Zhang, K.

177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

link (url) [BibTex]

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]

Variational Inference in Dynamical Systems

Ialongo, A.

University of Cambridge, UK, Cambridge, February 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

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

[BibTex]

Causal Models for Dynamical Systems

Peters, J., Bauer, S., Pfister, N.

In Probabilistic and Causal Inference: The Works of Judea Pearl, pages: 671-690, 1, Association for Computing Machinery, 2022 (inbook)

arXiv DOI [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]

Towards Causal Algorithmic Recourse

Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I.

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]

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]

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Salewski, L., Koepke, A. S., Lensch, H. P. A., Akata, Z.

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]

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]

Causality for Machine Learning

Schölkopf, B.

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]

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]