# publications

## Preprints

- Niles-Weed, J., and Zadik, I. (2021), “It was ‘all’ for ‘nothing’: sharp phase transitions for noiseless discrete channels.” [PDF]
- Chen, H.-B., Chewi, S., and Niles-Weed, J. (2021), “Dimension-free log-Sobolev inequalities for mixture distributions.” [PDF]
- Huang, D., Niles-Weed, J., and Ward, R. (2021), “Streaming k-PCA: Efficient guarantees for Oja’s algorithm, beyond rank-one updates.” [PDF]
- Altschuler, D. J., and Niles-Weed, J. (2021), “The Discrepancy of Random Rectangular Matrices.” [PDF]
- Mena, G., Nejatbakhsh, A., Varol, E., and Niles-Weed, J. (2020), “Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport.” [PDF]
- Huang, D., Niles-Weed, J., Tropp, J. A., and Ward, R. (2020), “Matrix Concentration for Products.” [video] [PDF]
- Niles-Weed, J., and Rigollet, P. (2019), “Estimation of Wasserstein distances in the Spiked Transport Model.” [video] [PDF]
- Bandeira, A. S., Blum-Smith, B., Kileel, J., Perry, A., Weed, J., and Wein, A. S. (2017), “Estimation under group actions: recovering orbits from invariants.” [PDF]

## Conference Articles

- Niles-Weed, J., and Zadik, I. (2020), “The All-or-Nothing Phenomenon in Sparse Tensor PCA,” in
*Advances in Neural Information Processing Systems 34 (NeurIPS 2020)*. [PDF] - 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 34 (NeurIPS 2020)*. [PDF] - 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] - 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] - 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 33 (NeurIPS 2019)*. [PDF] (Selected for spotlight presentation) - Weed, J., and Berthet, Q. (2019), “Estimation of smooth densities in Wasserstein distance,” in
*Proceedings of the 32nd Conference On Learning Theory (COLT 2019)*. [PDF] - 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)*. - 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] - 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] - Mao, C., Weed, J., and Rigollet, P. (2018), “Minimax rates and efficient algorithms for noisy sorting,” in
*Algorithmic Learning Theory (ALT 2018)*. [PDF] - 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) - 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

- Chen, H.-B., and Niles-Weed, J. (2021), “Asymptotics of smoothed Wasserstein distances,”
*Potential Analysis*. To appear. [PDF] - 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). - 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] - 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] - Rigollet, P., and Weed, J. (2019), “Uncoupled isotonic regression via minimum Wasserstein deconvolution,”
*Inf. Inference*, 8(4), 691–717. [video] [PDF] - 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] - Bandeira, A., Rigollet, P., and Weed, J. (2019), “Optimal rates of estimation for multi-reference alignment,”
*Mathematical Statistics and Learning*, 2, 25–75. [PDF] - Weed, J. (2018), “Approximately certifying the restricted isometry property is hard,”
*IEEE Trans. Inform. Theory*, 64(8), 5488–5497. - 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). - Rigollet, P., and Weed, J. (2018), “Entropic optimal transport is maximum-likelihood deconvolution,”
*Comptes Rendus Mathématique*, 356(11-12), 1228–1235. - 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] - 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

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

## Miscellaneous

- Weed, J. (2018), “Sharper rates for estimating differential entropy under Gaussian convolutions.” [PDF]