Please see Google Scholar for an up-to-date list of publications.

Preprints

  1. Kassraie, P., Pooladian, A.-A., Klein, M., Thornton, J., Niles-Weed, J., and Cuturi, M. (2024), “Progressive Entropic Optimal Transport Solvers.” [PDF]
  2. Divol, V., Niles-Weed, J., and Pooladian, A.-A. (2024), “Tight stability bounds for entropic Brenier maps.” [PDF]
  3. Klein, M., Pooladian, A.-A., Ablin, P., Ndiaye, E., Niles-Weed, J., and Cuturi, M. (2023), “Learning Costs for Structured Monge Displacements.” [PDF]
  4. Mossel, E., Niles-Weed, J., Sun, N., and Zadik, I. (2022), “On the Second Kahn–Kalai Conjecture.” [PDF]
  5. Gonzalez-Sanz, A., Loubes, J.-M., and Niles-Weed, J. (2022), “Weak limits of entropy regularized Optimal Transport; potentials, plans and divergences.” [PDF]
  6. Bing, X., Bunea, F., and Niles-Weed, J. (2022), “The Sketched Wasserstein Distance for mixture distributions.” [PDF]
  7. Divol, V., Niles-Weed, J., and Pooladian, A.-A. (2022), “Optimal transport map estimation in general function spaces.” [PDF]
  8. Bryan, J. G., Niles-Weed, J., and Hoff, P. D. (2021), “The multirank likelihood for semiparametric canonical correlation analysis.” [PDF]
  9. Pooladian, A.-A., and Niles-Weed, J. (2021), “Entropic estimation of optimal transport maps.” [PDF] (Best paper at Optimal Transport and Machine Learning workshop, NeurIPS 2021)

Conference Articles

  1. Frank, N. S., and Niles-Weed, J. (2023), “The Adversarial Consistency of Surrogate Risks for Binary Classification,” in Advances in Neural Information Processing Systems 36 (NeurIPS 2023). [PDF]
  2. Liu, S., Bunea, F., and Niles-Weed, J. (2023), “Asymptotic confidence sets for random linear programs,” in Conference on Learning Theory (COLT 2023). [PDF]
  3. Pooladian, A.-A., Divol, V., and Niles-Weed, J. (2023), “Minimax estimation of discontinuous optimal transport maps: The semi-discrete case,” in Fortieth International Conference on Machine Learning (ICML 2023). [PDF]
  4. Mossel, E., Niles-Weed, J., Sohn, Y., Sun, N., and Zadik, I. (2023), “Sharp thresholds in inference of planted subgraphs,” in Conference on Learning Theory (COLT 2023). [PDF]
  5. Ding, Y., and Niles-Weed, J. (2022), “Asymptotics of smoothed Wasserstein distances in the small noise regime,” in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). [PDF]
  6. Xi, J., and Niles-Weed, J. (2022), “Distributional Convergence of the Sliced Wasserstein Process,” in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). [PDF]
  7. Kunisky, D., and Niles-Weed, J. (2022), “Strong recovery of geometric planted matchings,” in ACM-SIAM Symposium on Discrete Algorithms (SODA22). [PDF]
  8. Pooladian, A.-A., Cuturi, M., and Niles-Weed, J. (2022), “Debiaser Beware: Pitfalls of Centering Regularized Transport Maps,” in Thirty-ninth International Conference on Machine Learning (ICML 2022). [PDF]
  9. Liu, S., Kaku, A., Zhu, W., Leibovich, M., Mohan, S., Yu, B., Huang, H., Zanna, L., Razavian, N., Niles-Weed, J., and Fernandez-Granda, C. (2021), “Deep Probability Estimation,” in Thirty-ninth International Conference on Machine Learning (ICML 2022). [PDF]
  10. Niles-Weed, J., and Zadik, I. (2021), “It was ‘all’ for ‘nothing’: sharp phase transitions for noiseless discrete channels,” in Conference on Learning Theory (COLT 2021). [PDF]
  11. Huang, D., Niles-Weed, J., and Ward, R. (2021), “Streaming k-PCA: Efficient guarantees for Oja’s algorithm, beyond rank-one updates,” in Conference on Learning Theory (COLT 2021). [PDF]
  12. Liu, S., Niles-Weed, J., Razavian, N., and Fernandez-Granda, C. (2020), “Early-Learning Regularization Prevents Memorization of Noisy Labels,” in Advances in Neural Information Processing Systems 33 (NeurIPS 2020). [PDF]
  13. Niles-Weed, J., and Zadik, I. (2020), “The All-or-Nothing Phenomenon in Sparse Tensor PCA,” in Advances in Neural Information Processing Systems 33 (NeurIPS 2020). [PDF]
  14. Cuturi, M., Teboul, O., Niles-Weed, J., and Vert, J.-P. (2020), “Supervised Quantile Normalization for Low-rank Matrix Approximation,” in Thirty-seventh International Conference on Machine Learning (ICML 2020). [PDF]
  15. Mena, G., and Weed, J. (2019), “Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019). [PDF] (Selected for spotlight presentation)
  16. Altschuler, J., Bach, F., Rudi, A., and Weed, J. (2019), “Massively scalable Sinkhorn distances via the Nyström method,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019). [PDF]
  17. Goldfeld, Z., Greenewald, K., Weed, J., and Polyanskiy, Y. (2019), “Optimality of the plug-in estimator for differential entropy estimation under Gaussian convolutions,” in 2019 IEEE International Symposium on Information Theory (ISIT).
  18. Weed, J., and Berthet, Q. (2019), “Estimation of smooth densities in Wasserstein distance,” in Proceedings of the 32nd Conference On Learning Theory (COLT 2019). (Superseded by journal version.) [PDF]
  19. Forrow, A., Hütter, J.-C., Nitzan, M., Rigollet, P., Schiebinger, G., and Weed, J. (2019), “Statistical optimal transport via factored couplings,” in 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). [PDF]
  20. Weed, J. (2018), “An explicit analysis of the entropic penalty in linear programming,” in Proceedings of the 31st Conference On Learning Theory (COLT 2018). [video] [PDF]
  21. Mao, C., Weed, J., and Rigollet, P. (2018), “Minimax rates and efficient algorithms for noisy sorting,” in Algorithmic Learning Theory (ALT 2018). [PDF]
  22. Altschuler, J., Weed, J., and Rigollet, P. (2017), “Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration,” in Advances in Neural Information Processing Systems 30 (NIPS 2017). [PDF] (Selected for spotlight presentation)
  23. Weed, J., Perchet, V., and Rigollet, P. (2016), “Online learning in repeated auctions,” in Proceedings of the 29th Conference on Learning Theory (COLT 2016). [video] [PDF]

Journal Articles

  1. Manole, T., Balakrishnan, S., Niles-Weed, J., and Wasserman, L. (2024+), “Plugin Estimation of Smooth Optimal Transport Maps,” Annals of Statistics. To appear. [PDF]
  2. Manole, T., and Niles-Weed, J. (2024), “Sharp Convergence Rates for Empirical Optimal Transport with Smooth Costs,” Annals of Applied Probability, 34(1B), 1108–1135. [PDF]
  3. Frank, N. S., and Niles-Weed, J. (2024), “Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification,” Journal of Machine Learning Research, 25(58), 1–41. [PDF]
  4. Bandeira, A. S., Blum-Smith, B., Kileel, J., Perry, A., Weed, J., and Wein, A. S. (2023), “Estimation under group actions: recovering orbits from invariants,” Applied and Computational Harmonic Analysis, 66, 236–319. [PDF]
  5. Barrio, E. del, Gonzalez-Sanz, A., Loubes, J.-M., and Niles-Weed, J. (2023), “An improved central limit theorem and fast convergence rates for entropic transportation costs,” SIAM Journal on Mathematics of Data Science, 5(3). [PDF]
  6. Carleton, W. C., Klassen, S., Niles-Weed, J., Evans, D., Roberts, P., and Groucutt, H. S. (2023), “Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor,” Scientific Reports, 13(1), 17913.
  7. Niles-Weed, J., and Rigollet, P. (2022), “Estimation of Wasserstein distances in the Spiked Transport Model,” Bernoulli, 28(4). [video] [PDF]
  8. Altschuler, D. J., and Niles-Weed, J. (2022), “The Discrepancy of Random Rectangular Matrices,” Random Structures & Algorithms, 60, 551–593. [PDF]
  9. Niles-Weed, J., and Berthet, Q. (2022), “Minimax estimation of smooth densities in Wasserstein distance,” Annals of Statistics, 50(3), 1519–1540. [PDF]
  10. Huang, D., Niles-Weed, J., Tropp, J. A., and Ward, R. (2022), “Matrix Concentration for Products,” Foundations of Computational Mathematics, 22, 1767–1799. [video] [PDF]
  11. Altschuler, J. M., Niles-Weed, J., and Stromme, A. J. (2022), “Asymptotics for semi-discrete entropic optimal transport,” SIAM Journal on Mathematical Analysis, 54(2). [PDF]
  12. Chen, H.-B., Chewi, S., and Niles-Weed, J. (2021), “Dimension-free log-Sobolev inequalities for mixture distributions,” Journal of Functional Analysis, 281(11). [PDF]
  13. Chen, H.-B., and Niles-Weed, J. (2021), “Asymptotics of smoothed Wasserstein distances,” Potential Analysis. [PDF]
  14. Klassen, S., Carter, A. K., Evans, D. H., Ortman, S., Stark, M. T., Loyless, A. A., Polkinghorne, M., Heng, P., Hill, M., Wijker, P., Niles-Weed, J., Marriner, G. P., Pottier, C., and Fletcher, R. J. (2021), “Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex,” Science Advances, 7(19). [PDF]
  15. Goldfeld, Z., Greenewald, K., Polyanskiy, Y., and Weed, J. (2020), “Convergence of smoothed empirical measures with applications to entropy estimation,” IEEE Trans. Inform. Theory, 66(7), 4368–4391. [PDF]
  16. Perry, A., Weed, J., Bandeira, A. S., Rigollet, P., and Singer, A. (2019), “The Sample Complexity of Multireference Alignment,” SIAM J. Math. Data Sci., 1(3), 497–517. [PDF]
  17. Weed, J., and Bach, F. (2019), “Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance,” Bernoulli, 25(4A), 2620–2648. [PDF]
  18. Rigollet, P., and Weed, J. (2019), “Uncoupled isotonic regression via minimum Wasserstein deconvolution,” Inf. Inference, 8(4), 691–717. [video] [PDF]
  19. Bandeira, A., Rigollet, P., and Weed, J. (2019), “Optimal rates of estimation for multi-reference alignment,” Mathematical Statistics and Learning, 2, 25–75. [PDF]
  20. Weed, J. (2018), “Approximately certifying the restricted isometry property is hard,” IEEE Trans. Inform. Theory, 64(8), 5488–5497.
  21. Klassen, S., Weed, J., and Evans, D. (2018), “Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia,” PloS one, 13(11).
  22. Rigollet, P., and Weed, J. (2018), “Entropic optimal transport is maximum-likelihood deconvolution,” Comptes Rendus Mathématique, 356(11-12), 1228–1235.
  23. Sawhney, M., and Weed, J. (2017), “Further results on arc and bar \(k\)-visibility graphs,” The Minnesota Journal of Undergraduate Mathematics, 3(1). Project mentored through MIT PRIMES. [PDF]
  24. Woo, A. (2009), “Permutations with Kazhdan-Lusztig polynomial \(P_{id,w}(q)=1+q^h\),” Electronic Journal of Combinatorics, 16(2). With an appendix by S. Billey and J. Weed. [PDF]

Book Chapters

  1. Weed, J. (2017), “Multinational War is Hard,” in The Mathematics of Various Entertaining Subjects, eds. J. Beineke and J. Rosenhouse, Princeton. [PDF]

Miscellaneous

  1. Mena, G., Nejatbakhsh, A., Varol, E., and Niles-Weed, J. (2020), “Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport.” [PDF]
  2. Weed, J. (2018), “Sharper rates for estimating differential entropy under Gaussian convolutions.” [PDF]