Publications
Submitted/Under Revision Articles
(1) H. Jiang and A. Guntuboyina (2021) A nonparametric maximum likelihood approach to mixture of regression. Available at https://arxiv.org/abs/2108.09816
(2) J. A. Soloff, A. Guntuboyina and M. I. Jordan (2020) Covariance estimation under nonnegative partial correlations. Available at https://arxiv.org/pdf/2007.15252.pdf.
(3) G. Kur, F. Gao, A. Guntuboyina and B. Sen (2020) Convex regression in multidimensions: suboptimality of least squares estimators. Available at https://arxiv.org/abs/2006.02044.
Journal Papers
(27) J. A. Soloff, A. Guntuboyina and B. Sen (2021) Multivariate, heteroscedastic empirical Bayes via Nonparametric Maximum Likelihood. Available at https://arxiv.org/abs/2109.03466. Accepted for publication in Journal of the Royal Statistical Society: Series B.
(26) D. Ki, B. Fang and A. Guntuboyina (2021) MARS via LASSO. Available at https://arxiv.org/pdf/2111.11694.pdf. Accepted for publication in Annals of Statistics.
(25) A. K. H. Kim and A. Guntuboyina (2020) Minimax bounds for estimating multivariate Gaussian location mixtures. Accepted for publication in Electronic Journal of Statistics. Available at https://arxiv.org/abs/2012.00444.
(24) A. Ghosh, A. Pananjady, A. Guntuboyina and K. Ramachandran (2021) Max-affine regression: parameter estimation for Gaussian designs. Accepted for publication in IEEE Transactions on Information Theory Available at https://arxiv.org/abs/1906.09255.
(23) N. Deb, S. Saha, A. Guntuboyina and B. Sen (2018) Two-component Mixture Model in the presence of covariates. Accepted for publication in Journal of the American Statistical Association. Available at https://arxiv.org/abs/1810.07897.
(22) B. Fang, A. Guntuboyina and B. Sen (2021) Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation. Annals of Statistics, vol. 49, pages 769–792. Available at https://arxiv.org/abs/1903.01395.
(21) O. Y. Feng, A. Guntuboyina, A. K. H. Kim and R. J. Samworth (2021) Adaptation in multivariate log-concave density estimation. Annals of Statistics, vol. 49, pages 129—153. Available at https://arxiv.org/abs/1812.11634.
(20) J. A. Soloff, A. Guntuboyina and J. Pitman (2019) Distribution-free properties of isotonic regression. Electronic Journal of Statistics, vol. 13, pages 3243—3253. Available at https://arxiv.org/abs/1812.04249.
(19) S. Saha and A. Guntuboyina (2017) On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising. Annals of Statistics, vol. 48, pages 738-762. Available at https://arxiv.org/abs/1712.02009.
(18) A. Guntuboyina, D. Lieu, S. Chatterjee and B. Sen (2020) Adaptive risk bounds in univariate total variation denoising and trend filtering. Annals of Statistics, vol. 48, pages 205-229. Available at https://arxiv.org/abs/1702.05113.
(17) A. Guntuboyina and B. Sen (2018) Nonparametric Shape-restricted Regression. Statistical Science, vol. 33, pages 568—594. Available at https://arxiv.org/abs/1709.05707.
(16) B. Fang and A. Guntuboyina (2019) On the risk of convex-constrained least squares estimators under misspecification. Bernoulli, vol. 25, pages 2206-2244. Available at https://arxiv.org/abs/1706.04276.
(15) Y. Wei, M. Wainwright and A. Guntuboyina (2019) The geometry of hypothesis testing over convex cones: Generalized likelihood ratio tests and minimax radii. Annals of Statistics, vol. 47, pages 994–1024. Available at https://arxiv.org/abs/1703.06810.
(14) X. Chen, A. Guntuboyina and Y. Zhang (2017) A note on the approximate admissibility of regularized estimators in the Gaussian sequence model. Electronic Journal of Statistics, vol. 11, pages 4746-4768. Available at https://arxiv.org/abs/1703.00542.
(13) A. K. H. Kim, A. Guntuboyina and R. J. Samworth (2018). Adaptation in log-concave density estimation. Annals of Statistics, Vol. 46, pages 2279—2306. Available at https://arxiv.org/abs/1609.00861.
(12) T. Cai, A. Guntuboyina and Y. Wei (2018). Adaptive estimation of planar convex sets. Available at http://arxiv.org/abs/1508.03744. Annals of Statistics, vol. 46, pages 1018-1049.
(11) X. Chen, A. Guntuboyina and Y. Zhang (2016). On Bayes risk lower bounds. Journal of Machine Learning Research, vol. 17, pages 1-58. Available at http://arxiv.org/abs/1410.0503.
(10) N. Shah, S. Balakrishnan, A. Guntuboyina and M. Wainwright (2017). Stochastically transitive models for pairwise comparisons: statistical and computational issues. IEEE Transactions on Information Theory, vol. 63, pages 934-959. Available at http://arxiv.org/abs/1510.05610.
(9) S. Chatterjee, A. Guntuboyina and B. Sen (2018). On matrix estimation under monotonicity constraints. Bernoulli, vol. 24, pages 1072-1100. Available at http://arxiv.org/abs/1506.03430.
(8) S. Chatterjee, A. Guntuboyina and B. Sen (2014). On risk bounds in isotonic and other shape restricted regression problems. Annals of Statistics. vol. 43, pages 1774-1800. Available at http://arxiv.org/pdf/1311.3765.pdf
(7) A. Guntuboyina (2016). Covering numbers of Lp balls of convex functions and sets. Constructive Approximation, vol. 43, pages 135-151. Available at http://arxiv.org/pdf/1403.6922.pdf.
(6) A. Guntuboyina and B. Sen (2015). Global risk bounds and adaptation in univariate convex regression. Probability Theory and Related Fields, vol. 163, pages 379-411. Available at http://arxiv.org/abs/1305.1648.
(5) A. Guntuboyina, S. Saha and G. Schiebinger (2014). Sharp inequalities between f-divergences. IEEE Transactions on Information Theory. vol. 60, pages 104-121.
(4) A. Guntuboyina and B. Sen (2013). Covering numbers for convex functions. IEEE Transactions on Information Theory. vol. 59, pages 1957-1965.
(3) A. Guntuboyina (2012). Optimal rates of convergence for convex set estimation from support functions. Annals of Statistics, vol. 40, pages 385-411.
(2) A. Guntuboyina (2011). Lower bounds for the minimax risk using f-divergences, and applications. IEEE Transactions on Information Theory, vol. 57, pages 2386-2399.
(1) A. Guntuboyina and H. Leeb (2009). Concentration of the spectral measure of large Wishart matrices with dependent entries. Electronic Communications in Probability, vol. 14, 334-342, 2009.
Peer-reviewed Conference Papers
(1) G. Kur, A. Guntuboyina and A. Rakhlin (2020). On suboptimality of least squares with application to estimation of convex bodies. Accepted for presentation and publication in the 33rd Annual Conference on Learning Theory (COLT) July 9-12, 2020.
(2) A. Ghosh, A. Pananjady, A. Guntuboyina and K. Ramachandran (2020). Max-affine regression with universal parameter estimation for small-ball designs. Accepted for publication in Proceedings of the 2020 IEEE International Symposium on Information Theory (ISIT).
(3) N. Shah, S. Balakrishnan, A. Guntuboyina and M. Wainwright (2016). Stochastically transitive models for pairwise comparisons: statistical and computational issues. Proceedings of the 33rd International Conference on Machine Learning, JMLR W&CP 48.
(4) A. Guntuboyina and B. Sen (2012). L1 covering numbers for Uniformly Bounded Convex Functions. Proceedings of the 2012 Conference on Learning Theory (COLT), JMLR W&CP 23: 12.1 - 12.13.
(5) A. Guntuboyina (2010). Minimax lower bounds via f-divergences. Proceedings of the 2010 IEEE International Symposium on Information Theory (ISIT), 1340 - 1344, Austin, TX.
PhD Thesis
A. Guntuboyina (2011). Minimax Lower Bounds. Ph.D. thesis, Yale University.