Patrick Forrรฉ
24 papers · 2019–2025 · 6 conferences · across top CS/AI conferences
Achievements
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๐ Academic Marathon (6) ๐ Interdisciplinary Bridge ๐ Conference Polyglot (6) ๐งญ Keyword Pioneer ๐ Cross-Pollinator (7)
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Renaissance Researcher
(5)
๐บ๏ธ
Taxonomy Completionist
(31)
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Interdisciplinary Bridge
๐
Conference Pioneer
๐๏ธ
Keyword Collector
(64)
๐
Trend Setter
๐ฅ
Unstoppable
(7)
๐
Century Club
(24)
โก
Prolific Year
(5)
Conferences
ICLR (7)
NIPS (6)
ICML (4)
UAI (4)
AISTATS (2)
CLEAR (1)
Top co-authors
Keywords
variational inference
(3)
normalizing flow
(3)
selection bia
(2)
bayesian inference
(2)
simulation-based inference
(2)
domain generalization
(2)
generative model
(2)
optimal transport
(1)
adversarial robustness
(1)
causal inference
(1)
density estimation
(1)
source separation
(1)
independent component analysis
(1)
data augmentation
(1)
image segmentation
(1)
multiclass classification
(1)
empirical risk minimization
(1)
pose estimation
(1)
mutual information
(1)
fair classification
(1)
Papers
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
ICML 2025
Robust Multi-view Co-expression Network Inference
CLEAR 2025
Lie Group Decompositions for Equivariant Neural Networks
ICLR 2024
Clifford-Steerable Convolutional Neural Networks
ICML 2024
Early-Exit Neural Networks with Nested Prediction Sets
UAI 2024
Clifford Group Equivariant Simplicial Message Passing Networks
ICLR 2024
Deep anytime-valid hypothesis testing
AISTATS 2024
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
ICLR 2024
Multi-objective optimization via equivariant deep hypervolume approximation
ICLR 2023
Clifford Group Equivariant Neural Networks
NIPS 2023
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
NIPS 2023
Equivariance-aware Architectural Optimization of Neural Networks
ICLR 2023
Multi-View Independent Component Analysis with Shared and Individual Sources
UAI 2023
Self-Supervised Inference in State-Space Models
ICLR 2022
Contrastive Neural Ratio Estimation
NIPS 2022
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
NIPS 2021
Selecting Data Augmentation for Simulating Interventions
ICML 2021
Self Normalizing Flows
ICML 2021
Truncated Marginal Neural Ratio Estimation
NIPS 2021
An Information-theoretic Approach to Distribution Shifts
NIPS 2021
Learning Robust Representations via Multi-View Information Bottleneck
ICLR 2020
Sinkhorn AutoEncoders
UAI 2019
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
UAI 2019
Reparameterizing Distributions on Lie Groups
AISTATS 2019