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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Identifiable Causal Representation Learning

von Kügelgen, J.

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

[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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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]

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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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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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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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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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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Dynamics of Learning and Learning of Dynamics

Mehrjou, A.

ETH Zürich, Zürich, October 2021 (phdthesis)

DOI [BibTex]

2021

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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On the Geometry of Data Representations

Bécigneul, G.

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

[BibTex]

[BibTex]


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Beyond traditional assumptions in fair machine learning

Kilbertus, N.

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

[BibTex]

[BibTex]


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Advances in Latent Variable and Causal Models

Rubenstein, P.

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

[BibTex]

[BibTex]


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Learning from Multi-Frame Data

Wieschollek, P.

University of Tübingen, Germany, July 2020 (phdthesis)

[BibTex]

[BibTex]


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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)

[BibTex]

[BibTex]

2019


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Robot Learning for Muscular Systems

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

link (url) [BibTex]

2019

link (url) [BibTex]


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Real Time Probabilistic Models for Robot Trajectories

Gomez-Gonzalez, S.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

[BibTex]

[BibTex]


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Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, February 2019 (phdthesis)

[BibTex]

[BibTex]


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Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

[BibTex]


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Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

[BibTex]

2018


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Optimal Trajectory Generation and Learning Control for Robot Table Tennis

Koc, O.

Technical University Darmstadt, Germany, 2018 (phdthesis)

[BibTex]

2018

[BibTex]


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Distribution-Dissimilarities in Machine Learning

Simon-Gabriel, C. J.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]


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Maschinelles Lernen: Entwicklung ohne Grenzen?

Schölkopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

[BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

[BibTex]


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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

[BibTex]

[BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]


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A machine learning approach to taking EEG-based computer interfaces out of the lab

Jayaram, V.

Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

[BibTex]

[BibTex]