Multinational War is Hard. - Jonathan Weed (CoRR, 2015)

Online learning in repeated auctions. - Jonathan Weed; Vianney Perchet; Philippe Rigollet (COLT, 2016)

Further results on arc and bar k-visibility graphs. - Mehtaab Sawhney; Jonathan Weed (CoRR, 2016)

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. - Jason M. Altschuler; Jonathan Weed; Philippe Rigollet (NIPS, 2017)

The sample complexity of multi-reference alignment. - Amelia Perry; Jonathan Weed; Afonso S. Bandeira; Philippe Rigollet; Amit Singer (CoRR, 2017)

Estimation under group actions: recovering orbits from invariants. - Afonso S. Bandeira; Ben Blum-Smith; Amelia Perry; Jonathan Weed; Alexander S. Wein (CoRR, 2017)

Minimax Rates and Efficient Algorithms for Noisy Sorting. - Cheng Mao; Jonathan Weed; Philippe Rigollet (ALT, 2018)

An explicit analysis of the entropic penalty in linear programming. - Jonathan Weed (COLT, 2018)

Statistical Optimal Transport via Geodesic Hubs. - Aden Forrow; Jan-Christian Hütter; Mor Nitzan; Geoffrey Schiebinger; Philippe Rigollet; Jonathan Weed (CoRR, 2018)

Approximating the Quadratic Transportation Metric in Near-Linear Time. - Jason M. Altschuler; Francis R. Bach; Alessandro Rudi; Jonathan Weed (CoRR, 2018)

Approximately Certifying the Restricted Isometry Property is Hard. - Jonathan Weed (IEEE Trans. Inf. Theory, 2018)

Statistical Optimal Transport via Factored Couplings. - Aden Forrow; Jan-Christian Hütter; Mor Nitzan; Philippe Rigollet; Geoffrey Schiebinger; Jonathan Weed (AISTATS, 2019)

Estimation of smooth densities in Wasserstein distance. - Jonathan Weed; Quentin Berthet (COLT, 2019)

Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions. - Ziv Goldfeld; Kristjan H. Greenewald; Jonathan Weed; Yury Polyanskiy (ISIT, 2019)

Massively scalable Sinkhorn distances via the Nyström method. - Jason M. Altschuler; Francis R. 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, 2019)

The Sample Complexity of Multireference Alignment. - Amelia Perry; Jonathan Weed; Afonso S. Bandeira; Philippe Rigollet; Amit Singer (SIAM J. Math. Data Sci., 2019)

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

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

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

Supervised Quantile Normalization for Low-rank Matrix Approximation. - Marco Cuturi; Olivier Teboul; Jonathan Niles-Weed; Jean-Philippe Vert (CoRR, 2020)

Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport. - Gonzalo E. Mena; Amin Nejatbakhsh; Erdem Varol; Jonathan Niles-Weed (CoRR, 2020)

Convergence of Smoothed Empirical Measures With Applications to Entropy Estimation. - Ziv Goldfeld; Kristjan H. Greenewald; Jonathan Niles-Weed; Yury Polyanskiy (IEEE Trans. Inf. Theory, 2020)

Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates. - De Huang; Jonathan Niles-Weed; Rachel A. Ward (COLT, 2021)

Deep Probability Estimation. - Sheng Liu; Aakash Kaku; Weicheng Zhu; Matan Leibovich; Sreyas Mohan; Boyang Yu 003; Haoxiang Huang; Laure Zanna; Narges Razavian; Jonathan Niles-Weed; Carlos Fernandez-Granda (ICML, 2022)

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

Asymptotics of smoothed Wasserstein distances in the small noise regime. - Yunzi Ding; Jonathan Niles-Weed (NeurIPS, 2022)

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

Strong recovery of geometric planted matchings. - Dmitriy Kunisky; Jonathan Niles-Weed (SODA, 2022)

On the Second Kahn-Kalai Conjecture. - Elchanan Mossel; Jonathan Niles-Weed; Nike Sun; Ilias Zadik (CoRR, 2022)

A second moment proof of the spread lemma. - Elchanan Mossel; Jonathan Niles-Weed; Nike Sun; Ilias Zadik (CoRR, 2022)

Matrix Concentration for Products. - De Huang; Jonathan Niles-Weed; Joel A. Tropp; Rachel A. Ward (Found. Comput. Math., 2022)

The discrepancy of random rectangular matrices. - Dylan J. Altschuler; Jonathan Niles-Weed (Random Struct. Algorithms, 2022)

Asymptotics for Semidiscrete Entropic Optimal Transport. - Jason M. Altschuler; Jonathan Niles-Weed; Austin J. Stromme (SIAM J. Math. Anal., 2022)

Asymptotic confidence sets for random linear programs. - Shuyu Liu; Florentina Bunea; Jonathan Niles-Weed (COLT, 2023)

Sharp thresholds in inference of planted subgraphs. - Elchanan Mossel; Jonathan Niles-Weed; Youngtak Sohn; Nike Sun; Ilias Zadik (COLT, 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 (ICML, 2023)

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

Learning Costs for Structured Monge Displacements. - Michal Klein; Aram-Alexandre Pooladian; Pierre Ablin; Eugène Ndiaye; Jonathan Niles-Weed; Marco Cuturi (CoRR, 2023)

It Was "All" for "Nothing": Sharp Phase Transitions for Noiseless Discrete Channels. - Jonathan Niles-Weed; Ilias Zadik (IEEE Trans. Inf. Theory, 2023)

An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs. - Eustasio del Barrio; Alberto González-Sanz; Jean-Michel Loubes; Jonathan Niles-Weed (SIAM J. Math. Data Sci., 2023)

Progressive Entropic Optimal Transport Solvers. - Parnian Kassraie; Aram-Alexandre Pooladian; Michal Klein; James Thornton; Jonathan Niles-Weed; Marco Cuturi (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)

Approximate independence of permutation mixtures. - Yanjun Han; Jonathan Niles-Weed (CoRR, 2024)

Plug-in estimation of Schrödinger bridges. - Aram-Alexandre Pooladian; Jonathan Niles-Weed (CoRR, 2024)

Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias. - Yifan Chen; Xiaoou Cheng 001; Jonathan Niles-Weed; Jonathan Weare (CoRR, 2024)

Learning large softmax mixtures with warm start EM. - Xin Bing; Florentina Bunea; Jonathan Niles-Weed; Marten H. Wegkamp (CoRR, 2024)

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification. - Natalie S. Frank; Jonathan Niles-Weed (J. Mach. Learn. Res., 2024)

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps. - Ricardo Baptista; Aram-Alexandre Pooladian; Michael Brennan; Youssef Marzouk 001; Jonathan Niles-Weed (AISTATS, 2025)

The multirank likelihood for semiparametric canonical correlation analysis. - Jordan Bryan; Jonathan Niles-Weed; Peter D. Hoff (J. Multivar. Anal., 2025)

A Bayesian Proof of the Spread Lemma. - Elchanan Mossel; Jonathan Niles-Weed; Nike Sun; Ilias Zadik (Random Struct. Algorithms, 2025)

Trajectory Inference with Smooth Schrödinger Bridges. - Wanli Hong; Yuliang Shi; Jonathan Niles-Weed (CoRR, 2025)