Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Enhancement and Evaluation of Deep Generative Networks with Applications in Super-Resolution and Image Generation

Sajjadi, S. M. M.

University of Tübingen, Germany, July 2024 (phdthesis)

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)

Advancing Normalising Flows to Model Boltzmann Distributions

Stimper, V.

University of Cambridge, UK, Cambridge, June 2024, (Cambridge-Tübingen-Fellowship-Program) (phdthesis)

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

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.

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)

Identifiable Causal Representation Learning

von Kügelgen, J.

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

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

2023

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

Jenny, D.

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

2023

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

Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness

Karimi, A.

ETH Zurich, Switzerland, July 2023 (phdthesis)

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

Learning and Testing Powerful Hypotheses

Kübler, J. M.

University of Tübingen, Germany, July 2023 (phdthesis)

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

Learning Identifiable Representations: Independent Influences and Multiple Views

Gresele, L.

University of Tübingen, Germany, June 2023 (phdthesis)

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

Learning with and for discrete optimization

Paulus, M.

ETH Zurich, Switzerland, May 2023, CLS PhD Program (phdthesis)

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

arxiv poster link (url) [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.

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)

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)

Towards learning mechanistic models at the right level of abstraction

Neitz, A.

University of Tübingen, Germany, November 2022 (phdthesis)

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

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)

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)

Learning Time-Continuous Dynamics Models with Gaussian-Process-Based Gradient Matching

Wenk, P.

ETH Zurich, Switzerland, October 2022, CLS PhD Program (phdthesis)

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

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]

Methods for Minimizing the Spread of Misinformation on the Web

Tabibian, B.

University of Tübingen, Germany, September 2022 (phdthesis)

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

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

Huang, B.

Carnegie Mellon University, Pittsburgh, USA, July 2022 (phdthesis)

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

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

Huang, B.

Carnegie Mellon University, July 2022, external supervision (phdthesis)

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

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)

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)

Variational Inference in Dynamical Systems

Ialongo, A.

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

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

2021

Dynamics of Learning and Learning of Dynamics

Mehrjou, A.

ETH Zürich, Zürich, October 2021 (phdthesis)

2021

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

A Large Scale Brain-Computer Interface for Patients with Neurological Diseases

Hohmann, M.

University of Tübingen, Germany, September 2021 (phdthesis)

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

Deep Learning Beyond The Training Distribution

Parascandolo, G.

ETH Zürich, Switzerland, Zürich, September 2021, (CLS Fellowship Program) (phdthesis)

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

Proceedings of the 1st Workshop on NLP for Positive Impact

Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C.

Association for Computational Linguistics, August 2021 (proceedings)

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)

Optimization Algorithms for Machine Learning

Raj, A.

University of Tübingen, Germany, June 2021 (phdthesis)

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

Causal Inference in Vision

Meding, K.

Eberhard Karls Universität Tübingen, Tübingen, June 2021 (phdthesis)

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

Machine Learning Methods for Modeling Synthesizable Molecules

Bradshaw, J.

University of Cambridge, UK, Cambridge, April 2021, (Cambridge-Tübingen-Fellowship) (phdthesis)

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

2020

Causal Feature Selection in Neuroscience

Mastakouri, A.

University of Tübingen, Germany, December 2020 (phdthesis)

2020

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

Enforcing and Discovering Structure in Machine Learning

Locatello, F.

ETH Zurich, Switzerland, November 2020, (CLS Fellowship Program) (phdthesis)

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

On the Geometry of Data Representations

Bécigneul, G.

ETH Zurich, Switzerland, September 2020, (CLS Fellowship Program) (phdthesis)

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

Beyond traditional assumptions in fair machine learning

Kilbertus, N.

University of Cambridge, UK, September 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)

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

Advances in Latent Variable and Causal Models

Rubenstein, P.

University of Cambridge, UK, July 2020, (Cambridge-Tuebingen-Fellowship) (phdthesis)

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

Learning from Multi-Frame Data

Wieschollek, P.

University of Tübingen, Germany, July 2020 (phdthesis)

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

Converting to Optimization in Machine Learning: Perturb-and-MAP, Differential Privacy, and Program Synthesis

Balog, M.

University of Cambridge, UK, July 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)

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)

2019

Robot Learning for Muscular Systems

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

2019

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