Jonathan Niles-Weed
Jonathan Niles-Weed
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Citata da
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
J Altschuler, J Weed, P Rigollet
Advances in Neural Information Processing Systems, 2017
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance
J Weed, F Bach
Bernoulli 25 (4A), 2620-2648, 2019
Massively scalable Sinkhorn distances via the Nyström method
J Altschuler, F Bach, A Rudi, J Weed
Advances in Neural Information Processing Systems, 2018
The sample complexity of multireference alignment
A Perry, J Weed, AS Bandeira, P Rigollet, A Singer
SIAM Journal on Mathematics of Data Science 1 (3), 497-517, 2019
Entropic optimal transport is maximum-likelihood deconvolution
P Rigollet, J Weed
Comptes Rendus Mathematique 356 (11-12), 1228-1235, 2018
Online learning in repeated auctions
J Weed, V Perchet, P Rigollet
Conference on Learning Theory, 1562-1583, 2016
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
G Mena, J Weed
arXiv preprint arXiv:1905.11882, 2019
Optimal rates of estimation for multi-reference alignment
AS Bandeira, P Rigollet, J Weed
Mathematical Statistics and Learning 2 (1), 25-75, 2017
Uncoupled isotonic regression via minimum Wasserstein deconvolution
P Rigollet, J Weed
Information and Inference: A Journal of the IMA 8 (4), 691-717, 2019
Early-learning regularization prevents memorization of noisy labels
S Liu, J Niles-Weed, N Razavian, C Fernandez-Granda
arXiv preprint arXiv:2007.00151, 2020
Estimation under group actions: recovering orbits from invariants
AS Bandeira, B Blum-Smith, J Kileel, A Perry, J Weed, AS Wein
arXiv preprint arXiv:1712.10163, 2017
Statistical optimal transport via factored couplings
A Forrow, JC Hütter, M Nitzan, P Rigollet, G Schiebinger, J Weed
The 22nd International Conference on Artificial Intelligence and Statistics …, 2018
Estimation of smooth densities in Wasserstein distance
J Weed, Q Berthet
Conference on Learning Theory, 3118-3119, 2019
Minimax rates and efficient algorithms for noisy sorting
C Mao, J Weed, P Rigollet
Algorithmic Learning Theory 83, 821–847, 2017
Convergence of smoothed empirical measures with applications to entropy estimation
Z Goldfeld, K Greenewald, J Niles-Weed, Y Polyanskiy
IEEE Transactions on Information Theory 66 (7), 4368-4391, 2020
Estimation of Wasserstein distances in the spiked transport model
J Niles-Weed, P Rigollet
arXiv preprint arXiv:1909.07513, 2019
Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia
S Klassen, J Weed, D Evans
PloS one 13 (11), e0205649, 2018
An explicit analysis of the entropic penalty in linear programming
J Weed
Conference on Learning Theory, 2018
Approximately certifying the restricted isometry property is hard
J Weed
IEEE Transactions on Information Theory 64 (8), 5488-5497, 2017
Matrix concentration for products
D Huang, J Niles-Weed, JA Tropp, R Ward
arXiv preprint arXiv:2003.05437, 2020
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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