Empirical Inference


2024


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Advances in Probabilistic Methods for Deep Learning

Immer, A.

ETH Zurich, Switzerland, September 2024, CLS PhD Program (phdthesis)

[BibTex]

2024

[BibTex]


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A Measure-Theoretic Axiomatisation of Causality and Kernel Regression

Park, J.

University of Tübingen, Germany, July 2024 (phdthesis)

[BibTex]

[BibTex]


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Advancing Normalising Flows to Model Boltzmann Distributions

Stimper, V.

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

[BibTex]

[BibTex]


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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]

link (url) [BibTex]


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Identifiable Causal Representation Learning

von Kügelgen, J.

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

[BibTex]

[BibTex]

2023


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Denoising Representation Learning for Causal Discovery

Sakenyte, U.

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

[BibTex]

2023

[BibTex]


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Efficient Sampling from Differentiable Matrix Elements

Kofler, A.

Technical University of Munich, Germany, September 2023 (mastersthesis)

[BibTex]

[BibTex]


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Learning and Testing Powerful Hypotheses

Kübler, J. M.

University of Tübingen, Germany, July 2023 (phdthesis)

[BibTex]

[BibTex]


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Learning Identifiable Representations: Independent Influences and Multiple Views

Gresele, L.

University of Tübingen, Germany, June 2023 (phdthesis)

[BibTex]


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Learning with and for discrete optimization

Paulus, M.

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

[BibTex]

[BibTex]


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Intrinsic complexity and mechanisms of expressivity of cortical neurons

Spieler, A. M.

University of Tübingen, Germany, March 2023 (mastersthesis)

[BibTex]

[BibTex]


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CausalEffect Estimation by Combining Observational and Interventional Data

Kladny, K.

ETH Zurich, Switzerland, February 2023 (mastersthesis)

[BibTex]


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Towards Generative Machine Teaching

Qui, Z.

Technical University of Munich, Germany, February 2023 (mastersthesis)

[BibTex]

[BibTex]


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ArchiSound: Audio Generation with Diffusion

Schneider, F.

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

[BibTex]

[BibTex]


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Generation and Quantification of Spin in Robot Table Tennis

Dittrich, A.

University of Stuttgart, Germany, January 2023 (mastersthesis)

[BibTex]

[BibTex]


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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]

DOI [BibTex]


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

2022


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Towards learning mechanistic models at the right level of abstraction

Neitz, A.

University of Tübingen, Germany, November 2022 (phdthesis)

[BibTex]

2022

[BibTex]


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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)

[BibTex]

[BibTex]


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Investigating Independent Mechanisms in Neural Networks

Liang, W.

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

[BibTex]

[BibTex]


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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)

[BibTex]

[BibTex]


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

Feil, M.

Technnical University Munich, Germany, September 2022 (mastersthesis)

[BibTex]

[BibTex]


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Independent Mechanism Analysis for High Dimensions

Sliwa, J.

University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (mastersthesis)

[BibTex]

[BibTex]


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Methods for Minimizing the Spread of Misinformation on the Web

Tabibian, B.

University of Tübingen, Germany, September 2022 (phdthesis)

[BibTex]

[BibTex]


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On the Adversarial Robustness of Causal Algorithmic Recourse

Dominguez-Olmedo, R.

University of Tübingen, Germany, August 2022 (mastersthesis)

[BibTex]

[BibTex]


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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)

[BibTex]

[BibTex]


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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)

[BibTex]

[BibTex]


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Independent Mechanism Analysis in High-Dimensional Observation Spaces

Ghosh, S.

ETH Zurich, Switzerland, June 2022 (mastersthesis)

[BibTex]


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Variational Inference in Dynamical Systems

Ialongo, A.

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

[BibTex]

[BibTex]


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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]

arXiv DOI [BibTex]


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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]

DOI [BibTex]


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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]

DOI [BibTex]


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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]

arXiv DOI [BibTex]

2021


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Learning Neural Causal Models with Active Interventions

Scherrer, N.

ETH Zurich, Switzerland, November 2021 (mastersthesis)

[BibTex]

2021

[BibTex]


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Study of the Interventional Consistency of Autoencoders

Lanzillotta, G.

ETH Zurich, Switzerland, October 2021 (mastersthesis)

[BibTex]

[BibTex]


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Dynamics of Learning and Learning of Dynamics

Mehrjou, A.

ETH Zürich, Zürich, October 2021 (phdthesis)

DOI [BibTex]

DOI [BibTex]


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

Hohmann, M.

University of Tübingen, Germany, September 2021 (phdthesis)

[BibTex]

[BibTex]


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Deep Learning Beyond The Training Distribution

Parascandolo, G.

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

DOI [BibTex]

DOI [BibTex]


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Optimization Algorithms for Machine Learning

Raj, A.

University of Tübingen, Germany, June 2021 (phdthesis)

[BibTex]

[BibTex]


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Causal Inference in Vision

Meding, K.

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

[BibTex]

[BibTex]


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Machine Learning Methods for Modeling Synthesizable Molecules

Bradshaw, J.

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

DOI [BibTex]

DOI [BibTex]

2020


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Causal Feature Selection in Neuroscience

Mastakouri, A.

University of Tübingen, Germany, December 2020 (phdthesis)

link (url) [BibTex]

2020

link (url) [BibTex]


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Enforcing and Discovering Structure in Machine Learning

Locatello, F.

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

[BibTex]

[BibTex]


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A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ahmed, O.

ETH Zurich, Switzerland, October 2020 (mastersthesis)

[BibTex]

[BibTex]