Niles-Weed, Jonathan

22 publications

AISTATS 2025 Conditional Simulation via Entropic Optimal Transport: Toward Non-Parametric Estimation of Conditional Brenier Maps Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan, Youssef Marzouk, Jonathan Niles-Weed

ICML 2025 Trajectory Inference with Smooth Schrödinger Bridges Wanli Hong, Yuliang Shi, Jonathan Niles-Weed

JMLR 2024 Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification Natalie S. Frank, Jonathan Niles-Weed

NeurIPS 2024 Learning Elastic Costs to Shape Monge Displacements Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

NeurIPS 2024 Progressive Entropic Optimal Transport Solvers Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

COLT 2023 Asymptotic Confidence Sets for Random Linear Programs Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed

ICML 2023 Minimax Estimation of Discontinuous Optimal Transport Maps: The Semi-Discrete Case Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed

ICML 2023 Perturbation Analysis of Neural Collapse Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

COLT 2023 Sharp Thresholds in Inference of Planted Subgraphs Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik

NeurIPS 2023 The Adversarial Consistency of Surrogate Risks for Binary Classification Natalie Frank, Jonathan Niles-Weed

NeurIPS 2022 Asymptotics of Smoothed Wasserstein Distances in the Small Noise Regime Yunzi Ding, Jonathan Niles-Weed

ICML 2022 Debiaser Beware: Pitfalls of Centering Regularized Transport Maps Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed

ICML 2022 Deep Probability Estimation Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda

NeurIPS 2022 Distributional Convergence of the Sliced Wasserstein Process Jiaqi Xi, Jonathan Niles-Weed

COLT 2021 It Was “all” for “nothing”: Sharp Phase Transitions for Noiseless Discrete Channels Jonathan Niles-Weed, Ilias Zadik

COLT 2021 Streaming K-PCA: Efficient Guarantees for Oja’s Algorithm, Beyond Rank-One Updates De Huang, Jonathan Niles-Weed, Rachel Ward

NeurIPS 2020 Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

ICML 2020 Supervised Quantile Normalization for Low Rank Matrix Factorization Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

NeurIPS 2020 The All-or-Nothing Phenomenon in Sparse Tensor PCA Jonathan Niles-Weed, Ilias Zadik

NeurIPS 2019 Massively Scalable Sinkhorn Distances via the Nyström Method Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed

NeurIPS 2019 Statistical Bounds for Entropic Optimal Transport: Sample Complexity and the Central Limit Theorem Gonzalo Mena, Jonathan Niles-Weed

NeurIPS 2017 Near-Linear Time Approximation Algorithms for Optimal Transport via Sinkhorn Iteration Jason Altschuler, Jonathan Niles-Weed, Philippe Rigollet