Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. - Patrick Forré; Joris M. Mooij (UAI, 2018)

Explorations in Homeomorphic Variational Auto-Encoding. - Luca Falorsi; Pim de Haan; Tim R. Davidson; Nicola De Cao; Maurice Weiler; Patrick Forré; Taco S. Cohen (CoRR, 2018)

Reparameterizing Distributions on Lie Groups. - Luca Falorsi; Pim de Haan; Tim R. Davidson; Patrick Forré (AISTATS, 2019)

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. - Patrick Forré; Joris M. Mooij (UAI, 2019)

Sinkhorn AutoEncoders. - Giorgio Patrini; Rianne van den Berg; Patrick Forré; Marcello Carioni; Samarth Bhargav 001; Max Welling; Tim Genewein; Frank Nielsen (UAI, 2019)

FlipOut: Uncovering Redundant Weights via Sign Flipping. - Andrei C. Apostol; Maarten C. Stol; Patrick Forré (BNAIC/BENELEARN (Selected Papers), 2020)

Learning Robust Representations via Multi-View Information Bottleneck. - Marco Federici; Anjan Dutta 001; Patrick Forré; Nate Kushman; Zeynep Akata (ICLR, 2020)

Designing Data Augmentation for Simulating Interventions. - Maximilian Ilse; Jakub M. Tomczak; Patrick Forré (CoRR, 2020)

Pruning via Iterative Ranking of Sensitivity Statistics. - Stijn Verdenius; Maarten Stol; Patrick Forré (CoRR, 2020)

Neural Ordinary Differential Equations on Manifolds. - Luca Falorsi; Patrick Forré (CoRR, 2020)

Improving Fair Predictions Using Variational Inference In Causal Models. - Rik Helwegen; Christos Louizos; Patrick Forré (CoRR, 2020)

Selecting Data Augmentation for Simulating Interventions. - Maximilian Ilse; Jakub M. Tomczak; Patrick Forré (ICML, 2021)

Self Normalizing Flows. - T. Anderson Keller 001; Jorn W. T. Peters; Priyank Jaini; Emiel Hoogeboom; Patrick Forré; Max Welling (ICML, 2021)

An Information-theoretic Approach to Distribution Shifts. - Marco Federici; Ryota Tomioka; Patrick Forré (NeurIPS, 2021)

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. - Emiel Hoogeboom; Didrik Nielsen; Priyank Jaini; Patrick Forré; Max Welling (NeurIPS, 2021)

Truncated Marginal Neural Ratio Estimation. - Benjamin Kurt Miller; Alex Cole; Patrick Forré; Gilles Louppe; Christoph Weniger (NeurIPS, 2021)

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. - Emiel Hoogeboom; Didrik Nielsen; Priyank Jaini; Patrick Forré; Max Welling (CoRR, 2021)

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. - Maximilian Ilse; Patrick Forré; Max Welling; Joris M. Mooij (CoRR, 2021)

Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. - Maurice Weiler; Patrick Forré; Erik Verlinde; Max Welling (CoRR, 2021)

Self-Supervised Hybrid Inference in State-Space Models. - David Ruhe; Patrick Forré (CoRR, 2021)

Self-Supervised Inference in State-Space Models. - David Ruhe; Patrick Forré (ICLR, 2022)

Contrastive Neural Ratio Estimation. - Benjamin Kurt Miller; Christoph Weniger; Patrick Forré (NeurIPS, 2022)

Pruning by leveraging training dynamics. - Andrei C. Apostol; Maarten C. Stol; Patrick Forré (AI Commun., 2022)

Detecting dispersed radio transients in real time using convolutional neural networks. - David Ruhe; Mark Kuiack; Antonia Rowlinson; Ralph A. M. J. Wijers; Patrick Forré (Astron. Comput., 2022)

Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. - Leon Lang; Pierre Baudot; Rick Quax; Patrick Forré (CoRR, 2022)

Architectural Optimization over Subgroups for Equivariant Neural Networks. - Kaitlin Maile; Dennis G. Wilson; Patrick Forré (CoRR, 2022)

Physics-informed inference of aerial animal movements from weather radar data. - Fiona Lippert; Bart Kranstauber; E. Emiel van Loon; Patrick Forré (CoRR, 2022)

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. - David Ruhe; Kaze Wong; Miles D. Cranmer; Patrick Forré (CoRR, 2022)

Multi-objective optimization via equivariant deep hypervolume approximation. - Jim Boelrijk; Bernd Ensing; Patrick Forré (ICLR, 2023)

Equivariance-aware Architectural Optimization of Neural Networks. - Kaitlin Maile; Dennis George Wilson; Patrick Forré (ICLR, 2023)

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. - Fiona Lippert; Bart Kranstauber; Emiel van Loon; Patrick Forré (NeurIPS, 2023)

Clifford Group Equivariant Neural Networks. - David Ruhe; Johannes Brandstetter; Patrick Forré (NeurIPS, 2023)

Multi-View Independent Component Analysis with Shared and Individual Sources. - Teodora Pandeva; Patrick Forré (UAI, 2023)

Balancing Simulation-based Inference for Conservative Posteriors. - Arnaud Delaunoy; Benjamin Kurt Miller; Patrick Forré; Christoph Weniger; Gilles Louppe (CoRR, 2023)

On the Effectiveness of Hybrid Mutual Information Estimation. - Marco Federici; David Ruhe; Patrick Forré (CoRR, 2023)

Simulation-based Inference with the Generalized Kullback-Leibler Divergence. - Benjamin Kurt Miller; Marco Federici; Christoph Weniger; Patrick Forré (CoRR, 2023)

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. - Metod Jazbec; Patrick Forré; Stephan Mandt; Dan Zhang; Eric T. Nalisnick (CoRR, 2023)

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. - Jim Boelrijk; Denice van Herwerden; Bernd Ensing; Patrick Forré; Saer Samanipour (J. Cheminformatics, 2023)

Deep anytime-valid hypothesis testing. - Teodora Pandeva; Patrick Forré; Aaditya Ramdas; Shubhanshu Shekhar (AISTATS, 2024)

Designing Long-term Group Fair Policies in Dynamical Systems. - Miriam Rateike; Isabel Valera; Patrick Forré (FAccT, 2024)

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. - Marco Federici; Patrick Forré; Ryota Tomioka; Bastiaan S. Veeling (ICLR, 2024)

Clifford Group Equivariant Simplicial Message Passing Networks. - Cong Liu; David Ruhe; Floor Eijkelboom; Patrick Forré (ICLR, 2024)

Lie Group Decompositions for Equivariant Neural Networks. - Mircea Mironenco; Patrick Forré (ICLR, 2024)

Clifford-Steerable Convolutional Neural Networks. - Maksim Zhdanov 001; David Ruhe; Maurice Weiler; Ana Lucic; Johannes Brandstetter; Patrick Forré (ICML, 2024)

Early-Exit Neural Networks with Nested Prediction Sets. - Metod Jazbec; Patrick Forré; Stephan Mandt; Dan Zhang; Eric T. Nalisnick (UAI, 2024)

Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks. - Cong Liu; David Ruhe; Patrick Forré (CoRR, 2024)

The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. - Lukas Fluri; Leon Lang; Alessandro Abate; Patrick Forré; David Krueger 001; Joar Skalse (CoRR, 2024)

Abstract Markov Random Fields. - Leon Lang; Clélia de Mulatier; Rick Quax; Patrick Forré (CoRR, 2024)

Towards detailed and interpretable hybrid modeling of continental-scale bird migration. - Fiona Lippert; Bart Kranstauber; Patrick Forré; E. Emiel van Loon (CoRR, 2024)

E-Valuating Classifier Two-Sample Tests. - Teodora Pandeva; Tim Bakker; Christian A. Naesseth; Patrick Forré (Trans. Mach. Learn. Res., 2024)

Robust Multi-view Co-expression Network Inference. - Teodora Pandeva; Martijs Jonker; Leendert Hamoen; Joris M. Mooij; Patrick Forré (CLeaR, 2025)

Modeling Human Beliefs about AI Behavior for Scalable Oversight. - Leon Lang; Patrick Forré (Trans. Mach. Learn. Res., 2025)

Clifford Group Equivariant Diffusion Models for 3D Molecular Generation. - Cong Liu; Sharvaree Vadgama; David Ruhe; Erik J. Bekkers; Patrick Forré (CoRR, 2025)

AdS-GNN - a Conformally Equivariant Graph Neural Network. - Maksim Zhdanov 001; Nabil Iqbal; Erik J. Bekkers; Patrick Forré (CoRR, 2025)