Jonathan Weed

According to our database, Jonathan Weed authored at least 42 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

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., September, 2023

It Was "All" for "Nothing": Sharp Phase Transitions for Noiseless Discrete Channels.

Jonathan Niles-Weed, Ilias Zadik

IEEE Trans. Inf. Theory, August, 2023

Learning Costs for Structured Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

CoRR, 2023

The Adversarial Consistency of Surrogate Risks for Binary Classification.

Natalie Frank, Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Perturbation Analysis of Neural Collapse.

Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

Proceedings of the International Conference on Machine Learning, 2023

Minimax estimation of discontinuous optimal transport maps: The semi-discrete case.

Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed

Proceedings of the International Conference on Machine Learning, 2023

Sharp thresholds in inference of planted subgraphs.

Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Asymptotic confidence sets for random linear programs.

Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Asymptotics for Semidiscrete Entropic Optimal Transport.

Jason M. Altschuler, Jonathan Niles-Weed, Austin J. Stromme

SIAM J. Math. Anal., 2022

The discrepancy of random rectangular matrices.

Dylan J. Altschuler, Jonathan Niles-Weed

Random Struct. Algorithms, 2022

Matrix Concentration for Products.

De Huang, Jonathan Niles-Weed, Joel A. Tropp, Rachel A. Ward

Found. Comput. Math., 2022

A second moment proof of the spread lemma.

Elchanan Mossel, Jonathan Niles-Weed, Nike Sun, Ilias Zadik

CoRR, 2022

On the Second Kahn-Kalai Conjecture.

Elchanan Mossel, Jonathan Niles-Weed, Nike Sun, Ilias Zadik

CoRR, 2022

Strong recovery of geometric planted matchings.

Dmitriy Kunisky, Jonathan Niles-Weed

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

Distributional Convergence of the Sliced Wasserstein Process.

Jiaqi Xi, Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Asymptotics of smoothed Wasserstein distances in the small noise regime.

Yunzi Ding, Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Debiaser Beware: Pitfalls of Centering Regularized Transport Maps.

Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2022

2021

Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates.

De Huang, Jonathan Niles-Weed, Rachel A. Ward

Proceedings of the Conference on Learning Theory, 2021

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

Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport.

Gonzalo E. Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed

CoRR, 2020

Supervised Quantile Normalization for Low-rank Matrix Approximation.

Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

CoRR, 2020

The All-or-Nothing Phenomenon in Sparse Tensor PCA.

Jonathan Niles-Weed, Ilias Zadik

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Early-Learning Regularization Prevents Memorization of Noisy Labels.

Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Supervised Quantile Normalization for Low Rank Matrix Factorization.

Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The Sample Complexity of Multireference Alignment.

Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer

SIAM J. Math. Data Sci., 2019

Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem.

Gonzalo Mena, Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Massively scalable Sinkhorn distances via the Nyström method.

Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions.

Ziv Goldfeld, Kristjan H. Greenewald, Jonathan Weed, Yury Polyanskiy

Proceedings of the IEEE International Symposium on Information Theory, 2019

Estimation of smooth densities in Wasserstein distance.

Jonathan Weed, Quentin Berthet

Proceedings of the Conference on Learning Theory, 2019

Statistical Optimal Transport via Factored Couplings.

Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Approximately Certifying the Restricted Isometry Property is Hard.

Jonathan Weed

IEEE Trans. Inf. Theory, 2018

Approximating the Quadratic Transportation Metric in Near-Linear Time.

Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed

CoRR, 2018

Statistical Optimal Transport via Geodesic Hubs.

Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Geoffrey Schiebinger, Philippe Rigollet, Jonathan Weed

CoRR, 2018

An explicit analysis of the entropic penalty in linear programming.

Jonathan Weed

Proceedings of the Conference On Learning Theory, 2018

Minimax Rates and Efficient Algorithms for Noisy Sorting.

Cheng Mao, Jonathan Weed, Philippe Rigollet

Proceedings of the Algorithmic Learning Theory, 2018

2017

Estimation under group actions: recovering orbits from invariants.

Afonso S. Bandeira, Ben Blum-Smith, Amelia Perry, Jonathan Weed, Alexander S. Wein

CoRR, 2017

The sample complexity of multi-reference alignment.

Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer

CoRR, 2017

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration.

Jason M. Altschuler, Jonathan Weed, Philippe Rigollet

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Further results on arc and bar k-visibility graphs.

Mehtaab Sawhney, Jonathan Weed

CoRR, 2016

Online learning in repeated auctions.

Jonathan Weed, Vianney Perchet, Philippe Rigollet

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Multinational War is Hard.

Jonathan Weed

CoRR, 2015