Gabriel Peyré
43 papers · 2014–2025 · 5 conferences · across top CS/AI conferences
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Conferences
NIPS (14)
ICML (13)
AISTATS (12)
ICLR (3)
COLT (1)
Top co-authors
Keywords
optimal transport
(14)
sinkhorn algorithm
(6)
entropic regularization
(6)
wasserstein distance
(6)
sinkhorn divergence
(4)
gromov-wasserstein distance
(3)
entropy regularization
(3)
kernel methods
(3)
gradient flow
(2)
generative model
(2)
residual network
(2)
metric learning
(2)
maximum mean discrepancy
(2)
wasserstein metric
(2)
bilevel optimization
(2)
point cloud
(2)
sample complexity
(2)
gradient descent
(2)
support recovery
(2)
convex optimization
(2)
Papers
Transformative or Conservative? Conservation laws for ResNets and Transformers
ICML 2025
Transformers are Universal In-context Learners
ICLR 2025
Towards Understanding the Universality of Transformers for Next-Token Prediction
ICLR 2025
How Smooth Is Attention?
ICML 2024
How do Transformers Perform In-Context Autoregressive Learning ?
ICML 2024
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
ICML 2024
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport
AISTATS 2024
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
AISTATS 2024
Sparsistency for inverse optimal transport
ICLR 2024
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
ICML 2023
Abide by the law and follow the flow: conservation laws for gradient flows
NIPS 2023
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe
AISTATS 2022
On global convergence of ResNets: From finite to infinite width using linear parameterization
NIPS 2022
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
NIPS 2022
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
ICML 2022
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
ICML 2022
Randomized Stochastic Gradient Descent Ascent
AISTATS 2022
Sinkformers: Transformers with Doubly Stochastic Attention
AISTATS 2022
Fast and accurate optimization on the orthogonal manifold without retraction
AISTATS 2022
Smooth Bilevel Programming for Sparse Regularization
NIPS 2021
Low-Rank Sinkhorn Factorization
ICML 2021
Momentum Residual Neural Networks
ICML 2021
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
NIPS 2021
Wasserstein Control of Mirror Langevin Monte Carlo
COLT 2020
Online Sinkhorn: Optimal Transport distances from sample streams
NIPS 2020
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
NIPS 2020
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
NIPS 2020
Super-efficiency of automatic differentiation for functions defined as a minimum
ICML 2020
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
AISTATS 2019
Sample Complexity of Sinkhorn Divergences
AISTATS 2019
Stochastic Deep Networks
ICML 2019
Geometric Losses for Distributional Learning
ICML 2019
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization
AISTATS 2019
Model Consistency for Learning with Mirror-Stratifiable Regularizers
AISTATS 2019
Universal Invariant and Equivariant Graph Neural Networks
NIPS 2019
Learning Generative Models with Sinkhorn Divergences
AISTATS 2018
Fast Dictionary Learning with a Smoothed Wasserstein Loss
AISTATS 2016
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
ICML 2016
Stochastic Optimization for Large-scale Optimal Transport
NIPS 2016
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
NIPS 2016
Sparse Support Recovery with Non-smooth Loss Functions
NIPS 2016
Biologically Inspired Dynamic Textures for Probing Motion Perception
NIPS 2015
Local Linear Convergence of Forward--Backward under Partial Smoothness
NIPS 2014