# publications

## Preprints & Technical Reports

- Niles-Weed, J., and Rigollet, P. (2019), “Estimation of Wasserstein distances in the Spiked Transport Model.” [PDF]
- Goldfeld, Z., Greenewald, K., Polyanskiy, Y., and Weed, J. (2019), “Convergence of smoothed empirical measures with applications to entropy estimation.” [PDF]
- Weed, J. (2018), “Sharper rates for estimating differential entropy under Gaussian convolutions.” [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]
- Bandeira, A., Rigollet, P., and Weed, J. (2017), “Optimal rates of estimation for multi-reference alignment.” [PDF]

## Conference Articles

- 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)*. To appear. [PDF] (Selected for spotlight presentation) - 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)*. To appear. [PDF] - Weed, J., and Berthet, Q. (2019), “Estimation of smooth densities in Wasserstein distance,” in
*Proceedings of the 32nd Conference On Learning Theory (COLT 2019)*. To appear. [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)*. To appear. - 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

- 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] - 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] - Rigollet, P., and Weed, J. (2018), “Entropic optimal transport is maximum-likelihood deconvolution,”
*Comptes Rendus Mathématique*, 356(11-12), 1228–1235. - 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). - Weed, J. (2018), “Approximately certifying the restricted isometry property is hard,”
*IEEE Trans. Inform. Theory*, 64(8), 5488–5497. - Rigollet, P., and Weed, J. (2018), “Uncoupled isotonic regression via minimum Wasserstein deconvolution,”
*Information and Inference: A Journal of the IMA*. To appear. [video] [PDF] - 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]