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)

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 (ICLR, 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 (ICLR, 2021)

On Disentangled Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Francesco Locatello; Andrea Dittadi; Anirudh Goyal; Bernhard Schölkopf; Stefan Bauer (ICML, 2021)

Backward-Compatible Prediction Updates: A Probabilistic Approach. - Frederik Träuble; Julius von Kügelgen; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Peter V. Gehler (NeurIPS, 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)

Boxhead: A Dataset for Learning Hierarchical Representations. - Yukun Chen; Frederik Träuble; Andrea Dittadi; Stefan Bauer; Bernhard Schölkopf (CoRR, 2021)

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. - Lukas Schott; Julius von Kügelgen; Frederik Träuble; Peter Vincent Gehler; Chris Russell 001; Matthias Bethge; Bernhard Schölkopf; Francesco Locatello; Wieland Brendel (ICLR, 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 (ICLR, 2022)

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks. - Davide Mambelli; Frederik Träuble; Stefan Bauer; Bernhard Schölkopf; Francesco Locatello (CoRR, 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)

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 (ICLR, 2023)

Discrete Key-Value Bottleneck. - Frederik Träuble; Anirudh Goyal; Nasim Rahaman; Michael Curtis Mozer; Kenji Kawaguchi; Yoshua Bengio; Bernhard Schölkopf (ICML, 2023)

Unlearning via Sparse Representations. - Vedant Shah; Frederik Träuble; Ashish Malik; Hugo Larochelle; Michael Mozer; Sanjeev Arora; Yoshua Bengio; Anirudh Goyal (CoRR, 2023)

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 001; Bernhard Schölkopf; Cornelia van Duijn; Debora S. Marks; Patrick Schwab (CoRR, 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 001; Bernhard Schölkopf (CoRR, 2025)