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ORCIDJonathan Weed
Person information
- affiliation: New York University, Courant Institute of Mathematical Sciences and Center for Data Science, USA
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2020 – today
- 2023
[j8]Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes
, Jonathan Niles-Weed:
An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs. SIAM J. Math. Data Sci. 5(3): 639-669 (2023)- [j7]Jonathan Niles-Weed, Ilias Zadik:
It Was "All" for "Nothing": Sharp Phase Transitions for Noiseless Discrete Channels. IEEE Trans. Inf. Theory 69(8): 5188-5202 (2023) - [c24]Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed:
Asymptotic confidence sets for random linear programs. COLT 2023: 3919-3940 - [c23]Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik:
Sharp thresholds in inference of planted subgraphs. COLT 2023: 5573-5577 - [c22]Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed:
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case. ICML 2023: 28128-28150 - [c21]Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed:
Perturbation Analysis of Neural Collapse. ICML 2023: 34301-34329 - [c20]Natalie Frank, Jonathan Niles-Weed:
The Adversarial Consistency of Surrogate Risks for Binary Classification. NeurIPS 2023 - [i29]Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik:
Sharp thresholds in inference of planted subgraphs. CoRR abs/2302.14830 (2023) - [i28]Natalie Frank, Jonathan Niles-Weed:
The Adversarial Consistency of Surrogate Risks for Binary Classification. CoRR abs/2305.09956 (2023) - [i27]Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi:
Learning Costs for Structured Monge Displacements. CoRR abs/2306.11895 (2023) - 2022
- [j6]De Huang, Jonathan Niles-Weed, Joel A. Tropp, Rachel A. Ward:
Matrix Concentration for Products. Found. Comput. Math. 22(6): 1767-1799 (2022) - [j5]Dylan J. Altschuler, Jonathan Niles-Weed:
The discrepancy of random rectangular matrices. Random Struct. Algorithms 60(4): 551-593 (2022) - [j4]Jason M. Altschuler, Jonathan Niles-Weed, Austin J. Stromme:
Asymptotics for Semidiscrete Entropic Optimal Transport. SIAM J. Math. Anal. 54(2): 1718-1741 (2022) - [c19]Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda:
Deep Probability Estimation. ICML 2022: 13746-13781 - [c18]Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed:
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps. ICML 2022: 17830-17847 - [c17]Yunzi Ding, Jonathan Niles-Weed:
Asymptotics of smoothed Wasserstein distances in the small noise regime. NeurIPS 2022 - [c16]Jiaqi Xi, Jonathan Niles-Weed:
Distributional Convergence of the Sliced Wasserstein Process. NeurIPS 2022 - [c15]Dmitriy Kunisky, Jonathan Niles-Weed:
Strong recovery of geometric planted matchings. SODA 2022: 834-876 - [i26]Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed:
An improved central limit theorem and fast convergence rates for entropic transportation costs. CoRR abs/2204.09105 (2022) - [i25]Elchanan Mossel, Jonathan Niles-Weed, Nike Sun, Ilias Zadik:
On the Second Kahn-Kalai Conjecture. CoRR abs/2209.03326 (2022) - [i24]Elchanan Mossel, Jonathan Niles-Weed, Nike Sun, Ilias Zadik:
A second moment proof of the spread lemma. CoRR abs/2209.11347 (2022) - [i23]Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed:
Perturbation Analysis of Neural Collapse. CoRR abs/2210.16658 (2022) - 2021
- [c14]De Huang, Jonathan Niles-Weed, Rachel A. Ward:
Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates. COLT 2021: 2463-2498 - [c13]Jonathan Niles-Weed, Ilias Zadik:
It was "all" for "nothing": sharp phase transitions for noiseless discrete channels. COLT 2021: 3546-3547 - [i22]Dylan J. Altschuler, Jonathan Niles-Weed:
The Discrepancy of Random Rectangular Matrices. CoRR abs/2101.04036 (2021) - [i21]De Huang, Jonathan Niles-Weed, Rachel A. Ward:
Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates. CoRR abs/2102.03646 (2021) - [i20]Jonathan Niles-Weed, Ilias Zadik:
It was "all" for "nothing": sharp phase transitions for noiseless discrete channels. CoRR abs/2102.12422 (2021) - [i19]Dmitriy Kunisky, Jonathan Niles-Weed:
Strong recovery of geometric planted matchings. CoRR abs/2107.05567 (2021) - 2020
- [j3]Ziv Goldfeld, Kristjan H. Greenewald, Jonathan Niles-Weed, Yury Polyanskiy:
Convergence of Smoothed Empirical Measures With Applications to Entropy Estimation. IEEE Trans. Inf. Theory 66(7): 4368-4391 (2020) - [c12]Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert:
Supervised Quantile Normalization for Low Rank Matrix Factorization. ICML 2020: 2269-2279 - [c11]Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda:
Early-Learning Regularization Prevents Memorization of Noisy Labels. NeurIPS 2020 - [c10]Jonathan Niles-Weed, Ilias Zadik:
The All-or-Nothing Phenomenon in Sparse Tensor PCA. NeurIPS 2020 - [i18]Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert:
Supervised Quantile Normalization for Low-rank Matrix Approximation. CoRR abs/2002.03229 (2020) - [i17]Gonzalo E. Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed:
Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport. CoRR abs/2006.16548 (2020) - [i16]Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda:
Early-Learning Regularization Prevents Memorization of Noisy Labels. CoRR abs/2007.00151 (2020) - [i15]Jonathan Niles-Weed, Ilias Zadik:
The All-or-Nothing Phenomenon in Sparse Tensor PCA. CoRR abs/2007.11138 (2020)
2010 – 2019
- 2019
- [j2]Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer:
The Sample Complexity of Multireference Alignment. SIAM J. Math. Data Sci. 1(3): 497-517 (2019) - [c9]Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed:
Statistical Optimal Transport via Factored Couplings. AISTATS 2019: 2454-2465 - [c8]Jonathan Weed, Quentin Berthet:
Estimation of smooth densities in Wasserstein distance. COLT 2019: 3118-3119 - [c7]Ziv Goldfeld, Kristjan H. Greenewald, Jonathan Weed, Yury Polyanskiy:
Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions. ISIT 2019: 892-896 - [c6]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed:
Massively scalable Sinkhorn distances via the Nyström method. NeurIPS 2019: 4429-4439 - [c5]Gonzalo Mena, Jonathan Niles-Weed:
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. NeurIPS 2019: 4543-4553 - [i14]Gonzalo Mena, Jonathan Weed:
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. CoRR abs/1905.11882 (2019) - [i13]Ziv Goldfeld, Kristjan H. Greenewald, Yury Polyanskiy, Jonathan Weed:
Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation. CoRR abs/1905.13576 (2019) - 2018
- [j1]Jonathan Weed:
Approximately Certifying the Restricted Isometry Property is Hard. IEEE Trans. Inf. Theory 64(8): 5488-5497 (2018) - [c4]Cheng Mao, Jonathan Weed, Philippe Rigollet:
Minimax Rates and Efficient Algorithms for Noisy Sorting. ALT 2018: 821-847 - [c3]Jonathan Weed:
An explicit analysis of the entropic penalty in linear programming. COLT 2018: 1841-1855 - [i12]Jonathan Weed:
An explicit analysis of the entropic penalty in linear programming. CoRR abs/1806.01879 (2018) - [i11]Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Geoffrey Schiebinger, Philippe Rigollet, Jonathan Weed:
Statistical Optimal Transport via Geodesic Hubs. CoRR abs/1806.07348 (2018) - [i10]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed:
Approximating the Quadratic Transportation Metric in Near-Linear Time. CoRR abs/1810.10046 (2018) - [i9]Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Weed:
Massively scalable Sinkhorn distances via the Nyström method. CoRR abs/1812.05189 (2018) - 2017
- [c2]Jason M. Altschuler, Jonathan Weed, Philippe Rigollet:
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. NIPS 2017: 1964-1974 - [i8]Jonathan Weed:
Approximately certifying the restricted isometry property is hard. CoRR abs/1704.00468 (2017) - [i7]Jason M. Altschuler, Jonathan Weed, Philippe Rigollet:
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. CoRR abs/1705.09634 (2017) - [i6]Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, Amit Singer:
The sample complexity of multi-reference alignment. CoRR abs/1707.00943 (2017) - [i5]Cheng Mao, Jonathan Weed, Philippe Rigollet:
Minimax Rates and Efficient Algorithms for Noisy Sorting. CoRR abs/1710.10388 (2017) - [i4]Afonso S. Bandeira, Ben Blum-Smith, Amelia Perry, Jonathan Weed, Alexander S. Wein:
Estimation under group actions: recovering orbits from invariants. CoRR abs/1712.10163 (2017) - 2016
- [c1]Jonathan Weed, Vianney Perchet, Philippe Rigollet:
Online learning in repeated auctions. COLT 2016: 1562-1583 - [i3]Mehtaab Sawhney, Jonathan Weed:
Further results on arc and bar k-visibility graphs. CoRR abs/1601.01231 (2016) - 2015
- [i2]Jonathan Weed:
Multinational War is Hard. CoRR abs/1503.00141 (2015) - [i1]Jonathan Weed, Vianney Perchet, Philippe Rigollet:
Online learning in repeated auctions. CoRR abs/1511.05720 (2015)
Coauthor Index
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