Frederik Träuble

According to our database, Frederik Träuble authored at least 18 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models.

Frederike Lübeck, Jonas Wildberger, Frederik Träuble, Maximilian Mordig, Sergios Gatidis, Andreas Krause, Bernhard Schölkopf

CoRR, May, 2025

Multi-megabase scale genome interpretation with genetic language models.

Frederik Träuble, Lachlan Stuart, Andreas Georgiou, Pascal Notin, Arash Mehrjou, Ron Schwessinger, Mathieu Chevalley, Kim Branson, Bernhard Schölkopf, Cornelia van Duijn, Debora S. Marks, Patrick Schwab

CoRR, January, 2025

Learning to Generalize Across Distribution Shifts.

Frederik Träuble

PhD thesis, 2025

Low Compute Unlearning via Sparse Representations.

Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Curtis Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal

Trans. Mach. Learn. Res., 2025

2023

Unlearning via Sparse Representations.

Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal

CoRR, 2023

Discrete Key-Value Bottleneck.

Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2023

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability.

Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A General Purpose Neural Architecture for Geospatial Systems.

Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf

CoRR, 2022

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.

Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello

CoRR, 2022

The Role of Pretrained Representations for the OOD Generalization of RL Agents.

Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

Proceedings of the Tenth International Conference on Learning Representations, 2022

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.

Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Boxhead: A Dataset for Learning Hierarchical Representations.

Yukun Chen, Frederik Träuble, Andrea Dittadi, Stefan Bauer, Bernhard Schölkopf

CoRR, 2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning.

Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

CoRR, 2021

Backward-Compatible Prediction Updates: A Probabilistic Approach.

Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Disentangled Representations Learned from Correlated Data.

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Transfer of Disentangled Representations in Realistic Settings.

Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

Proceedings of the 9th International Conference on Learning Representations, 2021

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning.

Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.

Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer

CoRR, 2020