2024
von Kügelgen, J.
Identifiable Causal Representation Learning
University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (phdthesis)
2023
Karimi, A.
Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness
ETH Zurich, Switzerland, July 2023 (phdthesis)
Kübler, J. M.
Learning and Testing Powerful Hypotheses
University of Tübingen, Germany, July 2023 (phdthesis)
Gresele, L.
Learning Identifiable Representations: Independent Influences and Multiple Views
University of Tübingen, Germany, June 2023 (phdthesis)
Paulus, M.
Learning with and for discrete optimization
(ETH Zurich, Switzerland), May 2023, CLS PhD Program (phdthesis)
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)
Bottou, L., Schölkopf, B.
Borges und die Künstliche Intelligenz
2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (misc)
2022
Neitz, A.
Towards learning mechanistic models at the right level of abstraction
University of Tübingen, Germany, November 2022 (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)
Tabibian, B.
Methods for Minimizing the Spread of Misinformation on the Web
University of Tübingen, Germany, September 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)
Ialongo, A.
Variational Inference in Dynamical Systems
University of Cambridge, UK, Cambridge, February 2022, (Cambridge-Tübingen-Fellowship) (phdthesis)
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)
2021
Mehrjou, A.
Dynamics of Learning and Learning of Dynamics
ETH Zürich, Zürich, October 2021 (phdthesis)
Hohmann, M.
A Large Scale Brain-Computer Interface for Patients with Neurological Diseases
University of Tübingen, Germany, September 2021 (phdthesis)
Parascandolo, G.
Deep Learning Beyond The Training Distribution
ETH Zürich, Switzerland, Zürich, September 2021, (CLS Fellowship Program) (phdthesis)
Raj, A.
Optimization Algorithms for Machine Learning
University of Tübingen, Germany, June 2021 (phdthesis)
Meding, K.
Causal Inference in Vision
Eberhard Karls Universität Tübingen, Tübingen, June 2021 (phdthesis)
Bradshaw, J.
Machine Learning Methods for Modeling Synthesizable Molecules
University of Cambridge, UK, Cambridge, April 2021, (Cambridge-Tübingen-Fellowship) (phdthesis)
Prabhoo, S., Bauer, S., Schwab, P.
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
2021 (misc)
Abdulsamad, H., Dorau, T., Belousov, B., Zhu, J., Peters, J.
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions
2021 (misc)
Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S.
Pulling back information geometry
2021 (misc)
Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., Srinivasa, S. S., Garg, A., Buchholz, A., Stark, S., Steinbrenner, T., Akpo, J., Joshi, S., Agrawal, V., Schölkopf, B.
A Robot Cluster for Reproducible Research in Dexterous Manipulation
2021, *equal contribution (misc)
Panda, S., Shen, C., Perry, R., Zorn, J., Lutz, A., Priebe, C. E., Vogelstein, J. T.
Nonpar MANOVA via Independence Testing
2021 (misc)
Shao, K., Villegas, J. F. R., Logothetis, N. K., Besserve, M.
A model of Ponto-Geniculo-Occipital waves supports bidirectional control of cortical plasticity across sleep-stages
2021 (misc) In preparation
Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G.
On the Impact of Stable Ranks in Deep Nets
2021 (misc)
Perry, R., Tomita, T. M., Mehta, R., Arroyo, J., Patsolic, J., Falk, B., Vogelstein, J. T.
Manifold forests: closing the gap on neural networks
2021 (misc)
Perry, R., Mehta, R., Guo, R., Yezerets, E., Arroyo, J., Powell, M., Helm, H., Shen, C., Vogelstein, J. T.
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
2021 (misc)
Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
2021 (misc)
2020
Mastakouri, A.
Causal Feature Selection in Neuroscience
University of Tübingen, Germany, December 2020 (phdthesis)
Locatello, F.
Enforcing and Discovering Structure in Machine Learning
ETH Zurich, Switzerland, November 2020, (CLS Fellowship Program) (phdthesis)
Bécigneul, G.
On the Geometry of Data Representations
ETH Zurich, Switzerland, September 2020, (CLS Fellowship Program) (phdthesis)
Kilbertus, N.
Beyond traditional assumptions in fair machine learning
University of Cambridge, UK, September 2020, (Cambridge-Tübingen-Fellowship) (phdthesis)
Rubenstein, P.
Advances in Latent Variable and Causal Models
University of Cambridge, UK, July 2020, (Cambridge-Tuebingen-Fellowship) (phdthesis)
Wieschollek, P.
Learning from Multi-Frame Data
University of Tübingen, Germany, July 2020 (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)
Chicharro, D., Besserve, M., Panzeri, S.
Causal learning with sufficient statistics: an information bottleneck approach
2020 (misc) Submitted
Jin, D., Jin, Z., Mihalcea, R.
Deep Learning for Text Attribute Transfer: A Survey
2020 (misc)
Tosatto, S., Stadtmueller, J., Peters, J.
Dimensionality Reduction of Movement Primitives in Parameter Space
2020 (misc)
Jin, Z., Yang, Y., Qiu, X., Zhang, Z.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
2020 (misc)
Ke, R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y.
Learning Neural Causal Models from Unknown Interventions
2020 (misc)
2019
Büchler, D.
Robot Learning for Muscular Systems
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Gomez-Gonzalez, S.
Real Time Probabilistic Models for Robot Trajectories
Technical University Darmstadt, Germany, December 2019 (phdthesis)
Meding, K., Schölkopf, B., Wichmann, F. A.
Perception of temporal dependencies in autoregressive motion
Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (poster)
Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A.
Phenomenal Causality and Sensory Realism
Perception, 48(2-suppl):141, 42nd European Conference on Visual Perception (ECVP), August 2019 (poster)
Lutz, P.
Automatic Segmentation and Labelling for Robot Table Tennis Time Series
Technical University Darmstadt, Germany, August 2019 (thesis)
Shao, K., Logothetis, N., Besserve, M.
Neural mass modeling of the Ponto-Geniculo-Occipital wave and its neuromodulation
28th Annual Computational Neuroscience Meeting (CNS*2019), July 2019 (poster)
Rojas-Carulla, M.
Learning Transferable Representations
University of Cambridge, UK, February 2019 (phdthesis)