Patrick Forré

82 papers A* 17A 7Journal 54Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	—	`book`	Equivariant and Coordinate Independent Convolutional Networks - A Gauge Field Theory of Neural Networks Equivariant and Coordinate Independent Convolutional Networks Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling
2026	J	`jnl`	Generalizing Coverage Plots for Simulation-based Inference. Trans. Mach. Learn. Res. Maximilian Lipp, Benjamin Kurt Miller, Lyubov Amitonova, Patrick Forré
2025	J	`jnl`	AdS-GNN - a Conformally Equivariant Graph Neural Network. CoRR Maksim Zhdanov, Nabil Iqbal, Erik J. Bekkers, Patrick Forré
2025	J	`jnl`	Clifford Group Equivariant Diffusion Models for 3D Molecular Generation. CoRR Cong Liu, Sharvaree Vadgama, David Ruhe, Erik J. Bekkers, Patrick Forré
2025	J	`jnl`	Frame-based Equivariant Diffusion Models for 3D Molecular Generation. CoRR Mohan Guo, Cong Liu, Patrick Forré
2025	J	`jnl`	Modeling Human Beliefs about AI Behavior for Scalable Oversight. CoRR Leon Lang, Patrick Forré
2025	J	`jnl`	Modeling Human Beliefs about AI Behavior for Scalable Oversight. Trans. Mach. Learn. Res. Leon Lang, Patrick Forré
2025	J	`jnl`	Möbius transforms and Shapley values for vector-valued functions on weighted directed acyclic multigraphs. CoRR Patrick Forré, Abel Jansma
2025	—	`conf`	Robust Multi-view Co-expression Network Inference. CLeaR Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré
2025	A*	`conf`	The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. ICML Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Max Viktor Skalse
2024	J	`jnl`	Abstract Markov Random Fields. CoRR Leon Lang, Clélia de Mulatier, Rick Quax, Patrick Forré
2024	A*	`conf`	Clifford Group Equivariant Simplicial Message Passing Networks. ICLR Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
2024	J	`jnl`	Clifford Group Equivariant Simplicial Message Passing Networks. CoRR Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
2024	A*	`conf`	Clifford-Steerable Convolutional Neural Networks. ICML Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré
2024	J	`jnl`	Clifford-Steerable Convolutional Neural Networks. CoRR Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré
2024	A	`conf`	Deep anytime-valid hypothesis testing. AISTATS Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
2024	—	`conf`	Designing Long-term Group Fair Policies in Dynamical Systems. FAccT Miriam Rateike, Isabel Valera, Patrick Forré
2024	J	`jnl`	E-Valuating Classifier Two-Sample Tests. Trans. Mach. Learn. Res. Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré
2024	A	`conf`	Early-Exit Neural Networks with Nested Prediction Sets. UAI Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
2024	A*	`conf`	Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
2024	A*	`conf`	Lie Group Decompositions for Equivariant Neural Networks. ICLR Mircea Mironenco, Patrick Forré
2024	J	`jnl`	Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks. CoRR Cong Liu, David Ruhe, Patrick Forré
2024	J	`jnl`	Robust Multi-view Co-expression Network Inference. CoRR Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré
2024	J	`jnl`	The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. CoRR Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Skalse
2024	J	`jnl`	Towards detailed and interpretable hybrid modeling of continental-scale bird migration. CoRR Fiona Lippert, Bart Kranstauber, Patrick Forré, E. Emiel van Loon
2023	J	`jnl`	Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. CoRR Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
2023	J	`jnl`	Balancing Simulation-based Inference for Conservative Posteriors. CoRR Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
2023	A*	`conf`	Clifford Group Equivariant Neural Networks. NeurIPS David Ruhe, Johannes Brandstetter, Patrick Forré
2023	J	`jnl`	Clifford Group Equivariant Neural Networks. CoRR David Ruhe, Johannes Brandstetter, Patrick Forré
2023	A*	`conf`	Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. NeurIPS Fiona Lippert, Bart Kranstauber, Emiel van Loon, Patrick Forré
2023	J	`jnl`	Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. CoRR Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré
2023	J	`jnl`	Deep anytime-valid hypothesis testing. CoRR Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
2023	J	`jnl`	Designing Long-term Group Fair Policies in Dynamical Systems. CoRR Miriam Rateike, Isabel Valera, Patrick Forré
2023	A*	`conf`	Equivariance-aware Architectural Optimization of Neural Networks. ICLR Kaitlin Maile, Dennis George Wilson, Patrick Forré
2023	J	`jnl`	Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
2023	J	`jnl`	Lie Group Decompositions for Equivariant Neural Networks. CoRR Mircea Mironenco, Patrick Forré
2023	A	`conf`	Multi-View Independent Component Analysis with Shared and Individual Sources. UAI Teodora Pandeva, Patrick Forré
2023	A*	`conf`	Multi-objective optimization via equivariant deep hypervolume approximation. ICLR Jim Boelrijk, Bernd Ensing, Patrick Forré
2023	J	`jnl`	On the Effectiveness of Hybrid Mutual Information Estimation. CoRR Marco Federici, David Ruhe, Patrick Forré
2023	J	`jnl`	Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. J. Cheminformatics Jim Boelrijk, Denice van Herwerden, Bernd Ensing, Patrick Forré, Saer Samanipour
2023	J	`jnl`	Simulation-based Inference with the Generalized Kullback-Leibler Divergence. CoRR Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré
2022	J	`jnl`	Architectural Optimization over Subgroups for Equivariant Neural Networks. CoRR Kaitlin Maile, Dennis G. Wilson, Patrick Forré
2022	A*	`conf`	Contrastive Neural Ratio Estimation. NeurIPS Benjamin Kurt Miller, Christoph Weniger, Patrick Forré
2022	J	`jnl`	Contrastive Neural Ratio Estimation. CoRR Benjamin Kurt Miller, Christoph Weniger, Patrick Forré
2022	J	`jnl`	Detecting dispersed radio transients in real time using convolutional neural networks. Astron. Comput. David Ruhe, Mark Kuiack, Antonia Rowlinson, Ralph A. M. J. Wijers, Patrick Forré
2022	J	`jnl`	E-Valuating Classifier Two-Sample Tests. CoRR Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré
2022	J	`jnl`	Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. CoRR Leon Lang, Pierre Baudot, Rick Quax, Patrick Forré
2022	J	`jnl`	Multi-View Independent Component Analysis with Shared and Individual Sources. CoRR Teodora Pandeva, Patrick Forré
2022	J	`jnl`	Multi-objective optimization via equivariant deep hypervolume approximation. CoRR Jim Boelrijk, Bernd Ensing, Patrick Forré
2022	J	`jnl`	Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré
2022	J	`jnl`	Physics-informed inference of aerial animal movements from weather radar data. CoRR Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré
2022	J	`jnl`	Pruning by leveraging training dynamics. AI Commun. Andrei C. Apostol, Maarten C. Stol, Patrick Forré
2022	A*	`conf`	Self-Supervised Inference in State-Space Models. ICLR David Ruhe, Patrick Forré
2021	A*	`conf`	An Information-theoretic Approach to Distribution Shifts. NeurIPS Marco Federici, Ryota Tomioka, Patrick Forré
2021	J	`jnl`	An Information-theoretic Approach to Distribution Shifts. CoRR Marco Federici, Ryota Tomioka, Patrick Forré
2021	A*	`conf`	Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
2021	J	`jnl`	Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
2021	J	`jnl`	Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. CoRR Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling
2021	J	`jnl`	Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij
2021	A*	`conf`	Selecting Data Augmentation for Simulating Interventions. ICML Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
2021	A*	`conf`	Self Normalizing Flows. ICML T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
2021	J	`jnl`	Self-Supervised Hybrid Inference in State-Space Models. CoRR David Ruhe, Patrick Forré
2021	A*	`conf`	Truncated Marginal Neural Ratio Estimation. NeurIPS Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
2021	J	`jnl`	Truncated Marginal Neural Ratio Estimation. CoRR Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
2020	J	`jnl`	Designing Data Augmentation for Simulating Interventions. CoRR Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
2020	—	`conf`	FlipOut: Uncovering Redundant Weights via Sign Flipping. BNAIC/BENELEARN (Selected Papers) Andrei C. Apostol, Maarten C. Stol, Patrick Forré
2020	J	`jnl`	FlipOut: Uncovering Redundant Weights via Sign Flipping. CoRR Andrei Apostol, Maarten Stol, Patrick Forré
2020	J	`jnl`	Improving Fair Predictions Using Variational Inference In Causal Models. CoRR Rik Helwegen, Christos Louizos, Patrick Forré
2020	A*	`conf`	Learning Robust Representations via Multi-View Information Bottleneck. ICLR Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata
2020	J	`jnl`	Learning Robust Representations via Multi-View Information Bottleneck. CoRR Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata
2020	J	`jnl`	Neural Ordinary Differential Equations on Manifolds. CoRR Luca Falorsi, Patrick Forré
2020	J	`jnl`	Pruning via Iterative Ranking of Sensitivity Statistics. CoRR Stijn Verdenius, Maarten Stol, Patrick Forré
2020	J	`jnl`	Self Normalizing Flows. CoRR T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
2019	A	`conf`	Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. UAI Patrick Forré, Joris M. Mooij
2019	J	`jnl`	Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. CoRR Patrick Forré, Joris M. Mooij
2019	A	`conf`	Reparameterizing Distributions on Lie Groups. AISTATS Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
2019	J	`jnl`	Reparameterizing Distributions on Lie Groups. CoRR Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
2019	A	`conf`	Sinkhorn AutoEncoders. UAI Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
2018	A	`conf`	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. UAI Patrick Forré, Joris M. Mooij
2018	J	`jnl`	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. CoRR Patrick Forré, Joris M. Mooij
2018	J	`jnl`	Explorations in Homeomorphic Variational Auto-Encoding. CoRR Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen
2018	J	`jnl`	Sinkhorn AutoEncoders. CoRR Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen