| 2025 | ICML | The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. | Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forr, David Krueger, Joar Max Viktor Skalse |
| 2024 | AISTATS | Deep anytime-valid hypothesis testing. | Teodora Pandeva, Patrick Forr, Aaditya Ramdas, Shubhanshu Shekhar |
| 2024 | ICLR | Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. | Marco Federici, Patrick Forr, Ryota Tomioka, Bastiaan S. Veeling |
| 2024 | ICLR | Clifford Group Equivariant Simplicial Message Passing Networks. | Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forr |
| 2024 | ICLR | Lie Group Decompositions for Equivariant Neural Networks. | Mircea Mironenco, Patrick Forr |
| 2024 | ICML | Clifford-Steerable Convolutional Neural Networks. | Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forr |
| 2024 | UAI | Early-Exit Neural Networks with Nested Prediction Sets. | Metod Jazbec, Patrick Forr, Stephan Mandt, Dan Zhang, Eric T. Nalisnick |
| 2023 | ICLR | Multi-objective optimization via equivariant deep hypervolume approximation. | Jim Boelrijk, Bernd Ensing, Patrick Forr |
| 2023 | ICLR | Equivariance-aware Architectural Optimization of Neural Networks. | Kaitlin Maile, Dennis George Wilson, Patrick Forr |
| 2023 | UAI | Multi-View Independent Component Analysis with Shared and Individual Sources. | Teodora Pandeva, Patrick Forr |
| 2022 | ICLR | Self-Supervised Inference in State-Space Models. | David Ruhe, Patrick Forr |
| 2021 | ICML | Selecting Data Augmentation for Simulating Interventions. | Maximilian Ilse, Jakub M. Tomczak, Patrick Forr |
| 2021 | ICML | Self Normalizing Flows. | T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forr, Max Welling |
| 2020 | ICLR | Learning Robust Representations via Multi-View Information Bottleneck. | Marco Federici, Anjan Dutta, Patrick Forr, Nate Kushman, Zeynep Akata |
| 2019 | AISTATS | Reparameterizing Distributions on Lie Groups. | Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forr |
| 2019 | UAI | Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. | Patrick Forr, Joris M. Mooij |
| 2019 | UAI | Sinkhorn AutoEncoders. | Giorgio Patrini, Rianne van den Berg, Patrick Forr, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen |
| 2018 | UAI | Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. | Patrick Forr, Joris M. Mooij |