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ORCIDPatrick Forré
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2020 – today
- 2024
[j4]Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré:
E-Valuating Classifier Two-Sample Tests. Trans. Mach. Learn. Res. 2024 (2024)- [c24]Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar:
Deep anytime-valid hypothesis testing. AISTATS 2024: 622-630 - [c23]Miriam Rateike
, Isabel Valera, Patrick Forré:
Designing Long-term Group Fair Policies in Dynamical Systems. FAccT 2024: 20-50 - [c22]Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling:
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR 2024 - [c21]Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré:
Clifford Group Equivariant Simplicial Message Passing Networks. ICLR 2024 - [c20]Mircea Mironenco, Patrick Forré:
Lie Group Decompositions for Equivariant Neural Networks. ICLR 2024 - [c19]Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré:
Clifford-Steerable Convolutional Neural Networks. ICML 2024 - [i43]Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré:
Clifford Group Equivariant Simplicial Message Passing Networks. CoRR abs/2402.10011 (2024) - [i42]Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré:
Clifford-Steerable Convolutional Neural Networks. CoRR abs/2402.14730 (2024) - [i41]Cong Liu, David Ruhe, Patrick Forré:
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks. CoRR abs/2406.04052 (2024) - [i40]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 abs/2406.15753 (2024) - [i39]Leon Lang, Clélia de Mulatier, Rick Quax, Patrick Forré:
Abstract Markov Random Fields. CoRR abs/2407.02134 (2024) - [i38]Fiona Lippert, Bart Kranstauber, Patrick Forré, E. Emiel van Loon:
Towards detailed and interpretable hybrid modeling of continental-scale bird migration. CoRR abs/2407.10259 (2024) - [i37]Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré:
Robust Multi-view Co-expression Network Inference. CoRR abs/2409.19991 (2024) - 2023
- [j3]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): 28 (2023) - [c18]Jim Boelrijk, Bernd Ensing, Patrick Forré:
Multi-objective optimization via equivariant deep hypervolume approximation. ICLR 2023 - [c17]Kaitlin Maile, Dennis George Wilson, Patrick Forré:
Equivariance-aware Architectural Optimization of Neural Networks. ICLR 2023 - [c16]Fiona Lippert, Bart Kranstauber, Emiel van Loon, Patrick Forré:
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. NeurIPS 2023 - [c15]David Ruhe, Johannes Brandstetter, Patrick Forré:
Clifford Group Equivariant Neural Networks. NeurIPS 2023 - [c14]Teodora Pandeva, Patrick Forré:
Multi-View Independent Component Analysis with Shared and Individual Sources. UAI 2023: 1639-1650 - [i36]Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe:
Balancing Simulation-based Inference for Conservative Posteriors. CoRR abs/2304.10978 (2023) - [i35]David Ruhe, Johannes Brandstetter, Patrick Forré:
Clifford Group Equivariant Neural Networks. CoRR abs/2305.11141 (2023) - [i34]Marco Federici, David Ruhe, Patrick Forré:
On the Effectiveness of Hybrid Mutual Information Estimation. CoRR abs/2306.00608 (2023) - [i33]Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré:
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. CoRR abs/2306.08445 (2023) - [i32]Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling:
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR abs/2309.07200 (2023) - [i31]Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré:
Simulation-based Inference with the Generalized Kullback-Leibler Divergence. CoRR abs/2310.01808 (2023) - [i30]Mircea Mironenco, Patrick Forré:
Lie Group Decompositions for Equivariant Neural Networks. CoRR abs/2310.11366 (2023) - [i29]Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar:
Deep anytime-valid hypothesis testing. CoRR abs/2310.19384 (2023) - [i28]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 abs/2311.05931 (2023) - [i27]Miriam Rateike, Isabel Valera, Patrick Forré:
Designing Long-term Group Fair Policies in Dynamical Systems. CoRR abs/2311.12447 (2023) - 2022
- [j2]Andrei C. Apostol, Maarten C. Stol, Patrick Forré:
Pruning by leveraging training dynamics. AI Commun. 35(2): 65-85 (2022) - [j1]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: 100512 (2022) - [c13]David Ruhe, Patrick Forré:
Self-Supervised Inference in State-Space Models. ICLR 2022 - [c12]Benjamin Kurt Miller, Christoph Weniger, Patrick Forré:
Contrastive Neural Ratio Estimation. NeurIPS 2022 - [i26]Leon Lang, Pierre Baudot, Rick Quax, Patrick Forré:
Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. CoRR abs/2202.09393 (2022) - [i25]Teodora Pandeva, Patrick Forré:
Multi-View Independent Component Analysis with Shared and Individual Sources. CoRR abs/2210.02083 (2022) - [i24]Jim Boelrijk, Bernd Ensing, Patrick Forré:
Multi-objective optimization via equivariant deep hypervolume approximation. CoRR abs/2210.02177 (2022) - [i23]Kaitlin Maile, Dennis G. Wilson, Patrick Forré:
Architectural Optimization over Subgroups for Equivariant Neural Networks. CoRR abs/2210.05484 (2022) - [i22]Benjamin Kurt Miller, Christoph Weniger, Patrick Forré:
Contrastive Neural Ratio Estimation. CoRR abs/2210.06170 (2022) - [i21]Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré:
E-Valuating Classifier Two-Sample Tests. CoRR abs/2210.13027 (2022) - [i20]Fiona Lippert, Bart Kranstauber, E. Emiel van Loon, Patrick Forré:
Physics-informed inference of aerial animal movements from weather radar data. CoRR abs/2211.04539 (2022) - [i19]David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré:
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR abs/2211.09008 (2022) - 2021
- [c11]Maximilian Ilse, Jakub M. Tomczak, Patrick Forré:
Selecting Data Augmentation for Simulating Interventions. ICML 2021: 4555-4562 - [c10]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. ICML 2021: 5378-5387 - [c9]Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger:
Truncated Marginal Neural Ratio Estimation. NeurIPS 2021: 129-143 - [c8]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS 2021: 12454-12465 - [c7]Marco Federici, Ryota Tomioka, Patrick Forré:
An Information-theoretic Approach to Distribution Shifts. NeurIPS 2021: 17628-17641 - [i18]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR abs/2102.05379 (2021) - [i17]Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij:
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR abs/2103.04786 (2021) - [i16]Marco Federici, Ryota Tomioka, Patrick Forré:
An Information-theoretic Approach to Distribution Shifts. CoRR abs/2106.03783 (2021) - [i15]Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling:
Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. CoRR abs/2106.06020 (2021) - [i14]Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger:
Truncated Marginal Neural Ratio Estimation. CoRR abs/2107.01214 (2021) - [i13]David Ruhe, Patrick Forré:
Self-Supervised Hybrid Inference in State-Space Models. CoRR abs/2107.13349 (2021) - 2020
- [c6]Andrei C. Apostol, Maarten C. Stol, Patrick Forré:
FlipOut: Uncovering Redundant Weights via Sign Flipping. BNAIC/BENELEARN (Selected Papers) 2020: 15-29 - [c5]Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata:
Learning Robust Representations via Multi-View Information Bottleneck. ICLR 2020 - [i12]Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata:
Learning Robust Representations via Multi-View Information Bottleneck. CoRR abs/2002.07017 (2020) - [i11]Maximilian Ilse, Jakub M. Tomczak, Patrick Forré:
Designing Data Augmentation for Simulating Interventions. CoRR abs/2005.01856 (2020) - [i10]Stijn Verdenius, Maarten Stol, Patrick Forré:
Pruning via Iterative Ranking of Sensitivity Statistics. CoRR abs/2006.00896 (2020) - [i9]Luca Falorsi, Patrick Forré:
Neural Ordinary Differential Equations on Manifolds. CoRR abs/2006.06663 (2020) - [i8]Rik Helwegen, Christos Louizos, Patrick Forré:
Improving Fair Predictions Using Variational Inference In Causal Models. CoRR abs/2008.10880 (2020) - [i7]Andrei Apostol, Maarten Stol, Patrick Forré:
FlipOut: Uncovering Redundant Weights via Sign Flipping. CoRR abs/2009.02594 (2020) - [i6]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. CoRR abs/2011.07248 (2020)
2010 – 2019
- 2019
- [c4]Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré:
Reparameterizing Distributions on Lie Groups. AISTATS 2019: 3244-3253 - [c3]Patrick Forré, Joris M. Mooij:
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. UAI 2019: 71-80 - [c2]Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. UAI 2019: 733-743 - [i5]Patrick Forré, Joris M. Mooij:
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. CoRR abs/1901.00433 (2019) - [i4]Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré:
Reparameterizing Distributions on Lie Groups. CoRR abs/1903.02958 (2019) - 2018
- [c1]Patrick Forré, Joris M. Mooij:
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. UAI 2018: 269-278 - [i3]Patrick Forré, Joris M. Mooij:
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. CoRR abs/1807.03024 (2018) - [i2]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 abs/1807.04689 (2018) - [i1]Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. CoRR abs/1810.01118 (2018)
Coauthor Index
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