Patrick Forré

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 69

Top Topics

Riemannian Manifolds

Top Venues

Publications

Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
Clifford Group Equivariant Simplicial Message Passing Networks. ICLR (2024)
Leon Lang, Clélia de Mulatier, Rick Quax, Patrick Forré
Abstract Markov Random Fields. CoRR (2024)
Fiona Lippert, Bart Kranstauber, Patrick Forré, E. Emiel van Loon
Towards detailed and interpretable hybrid modeling of continental-scale bird migration. CoRR (2024)
Mircea Mironenco, Patrick Forré
Lie Group Decompositions for Equivariant Neural Networks. ICLR (2024)
Cong Liu, David Ruhe, Patrick Forré
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks. CoRR (2024)
Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR (2024)
Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré
E-Valuating Classifier Two-Sample Tests. Trans. Mach. Learn. Res. 2024 (2024)
Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Skalse
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. CoRR (2024)
Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
Deep anytime-valid hypothesis testing. AISTATS (2024)
Miriam Rateike, Isabel Valera, Patrick Forré
Designing Long-term Group Fair Policies in Dynamical Systems. FAccT (2024)
Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré
Clifford-Steerable Convolutional Neural Networks. CoRR (2024)
Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
Clifford Group Equivariant Simplicial Message Passing Networks. CoRR (2024)
Jim Boelrijk, Bernd Ensing, Patrick Forré
Multi-objective optimization via equivariant deep hypervolume approximation. ICLR (2023)
Mircea Mironenco, Patrick Forré
Lie Group Decompositions for Equivariant Neural Networks. CoRR (2023)
Jim Boelrijk, Denice van Herwerden, Bernd Ensing, Patrick Forré, Saer Samanipour
Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. J. Cheminformatics 15 (1) (2023)
Kaitlin Maile, Dennis George Wilson, Patrick Forré
Equivariance-aware Architectural Optimization of Neural Networks. ICLR (2023)
Fiona Lippert, Bart Kranstauber, Emiel van Loon, Patrick Forré
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. NeurIPS (2023)
Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
Deep anytime-valid hypothesis testing. CoRR (2023)
Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR (2023)
Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré
Simulation-based Inference with the Generalized Kullback-Leibler Divergence. CoRR (2023)
Teodora Pandeva, Patrick Forré
Multi-View Independent Component Analysis with Shared and Individual Sources. UAI (2023)
Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. CoRR (2023)
David Ruhe, Johannes Brandstetter, Patrick Forré
Clifford Group Equivariant Neural Networks. NeurIPS (2023)
David Ruhe, Johannes Brandstetter, Patrick Forré
Clifford Group Equivariant Neural Networks. CoRR (2023)
Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. CoRR (2023)
Marco Federici, David Ruhe, Patrick Forré
On the Effectiveness of Hybrid Mutual Information Estimation. CoRR (2023)
Miriam Rateike, Isabel Valera, Patrick Forré
Designing Long-term Group Fair Policies in Dynamical Systems. CoRR (2023)
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
Balancing Simulation-based Inference for Conservative Posteriors. CoRR (2023)
Benjamin Kurt Miller, Christoph Weniger, Patrick Forré
Contrastive Neural Ratio Estimation. CoRR (2022)
Kaitlin Maile, Dennis G. Wilson, Patrick Forré
Architectural Optimization over Subgroups for Equivariant Neural Networks. CoRR (2022)
Benjamin Kurt Miller, Christoph Weniger, Patrick Forré
Contrastive Neural Ratio Estimation. NeurIPS (2022)
Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré
E-Valuating Classifier Two-Sample Tests. CoRR (2022)
David Ruhe, Mark Kuiack, Antonia Rowlinson, Ralph A. M. J. Wijers, Patrick Forré
Detecting dispersed radio transients in real time using convolutional neural networks. Astron. Comput. 38 (2022)
David Ruhe, Patrick Forré
Self-Supervised Inference in State-Space Models. ICLR (2022)
Jim Boelrijk, Bernd Ensing, Patrick Forré
Multi-objective optimization via equivariant deep hypervolume approximation. CoRR (2022)
Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré
Physics-informed inference of aerial animal movements from weather radar data. CoRR (2022)
Teodora Pandeva, Patrick Forré
Multi-View Independent Component Analysis with Shared and Individual Sources. CoRR (2022)
Leon Lang, Pierre Baudot, Rick Quax, Patrick Forré
Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. CoRR (2022)
David Ruhe, Kaze Wong, Miles Cranmer, Patrick Forré
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR (2022)
Andrei C. Apostol, Maarten C. Stol, Patrick Forré
Pruning by leveraging training dynamics. AI Commun. 35 (2) (2022)
David Ruhe, Patrick Forré
Self-Supervised Hybrid Inference in State-Space Models. CoRR (2021)
Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
Truncated Marginal Neural Ratio Estimation. NeurIPS (2021)
Marco Federici, Ryota Tomioka, Patrick Forré
An Information-theoretic Approach to Distribution Shifts. NeurIPS (2021)
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
Selecting Data Augmentation for Simulating Interventions. ICML (2021)
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
Self Normalizing Flows. ICML (2021)
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR (2021)
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS (2021)
Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling
Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. CoRR (2021)
Marco Federici, Ryota Tomioka, Patrick Forré
An Information-theoretic Approach to Distribution Shifts. CoRR (2021)
Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
Truncated Marginal Neural Ratio Estimation. CoRR (2021)
Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR (2021)
Andrei Apostol, Maarten Stol, Patrick Forré
FlipOut: Uncovering Redundant Weights via Sign Flipping. CoRR (2020)
Luca Falorsi, Patrick Forré
Neural Ordinary Differential Equations on Manifolds. CoRR (2020)
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata
Learning Robust Representations via Multi-View Information Bottleneck. ICLR (2020)
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
Self Normalizing Flows. CoRR (2020)
Rik Helwegen, Christos Louizos, Patrick Forré
Improving Fair Predictions Using Variational Inference In Causal Models. CoRR (2020)
Andrei C. Apostol, Maarten C. Stol, Patrick Forré
FlipOut: Uncovering Redundant Weights via Sign Flipping. BNAIC/BENELEARN (Selected Papers) (2020)
Stijn Verdenius, Maarten Stol, Patrick Forré
Pruning via Iterative Ranking of Sensitivity Statistics. CoRR (2020)
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
Designing Data Augmentation for Simulating Interventions. CoRR (2020)
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata
Learning Robust Representations via Multi-View Information Bottleneck. CoRR (2020)
Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
Sinkhorn AutoEncoders. UAI (2019)
Patrick Forré, Joris M. Mooij
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. CoRR (2019)
Patrick Forré, Joris M. Mooij
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. UAI (2019)
Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
Reparameterizing Distributions on Lie Groups. AISTATS (2019)
Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
Reparameterizing Distributions on Lie Groups. CoRR (2019)
Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen
Sinkhorn AutoEncoders. CoRR (2018)
Patrick Forré, Joris M. Mooij
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. CoRR (2018)
Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen
Explorations in Homeomorphic Variational Auto-Encoding. CoRR (2018)
Patrick Forré, Joris M. Mooij
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. UAI (2018)