| Reconciling modern machine-learning practice and the classical bias–variance trade-off M Belkin, D Hsu, S Ma, S Mandal Proceedings of the National Academy of Sciences 116 (32), 15849-15854, 2019 | 2880 | 2019 |
| Tensor decompositions for learning latent variable models. A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky J. Mach. Learn. Res. 15 (1), 2773-2832, 2014 | 1545 | 2014 |
| Certified robustness to adversarial examples with differential privacy M Lecuyer, V Atlidakis, R Geambasu, D Hsu, S Jana arXiv preprint arXiv:1802.03471, 2018 | 1327 | 2018 |
| Taming the monster: A fast and simple algorithm for contextual bandits A Agarwal, D Hsu, S Kale, J Langford, L Li, RE Schapire Thirty-First International Conference on Machine Learning, 2014 | 665 | 2014 |
| Hierarchical sampling for active learning S Dasgupta, D Hsu Proceedings of the 25th international conference on Machine learning, 208-215, 2008 | 657 | 2008 |
| A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:0811.4413, 2008 | 656* | 2008 |
| A tail inequality for quadratic forms of subgaussian random vectors D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:1110.2842, 2011 | 539 | 2011 |
| Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009 | 537 | 2009 |
| Two models of double descent for weak features M Belkin, D Hsu, J Xu SIAM Journal on Mathematics of Data Science 2 (4), 1167-1180, 2020 | 528 | 2020 |
| Learning mixtures of spherical gaussians: moment methods and spectral decompositions D Hsu, SM Kakade Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013 | 423 | 2013 |
| A general agnostic active learning algorithm S Dasgupta, D Hsu, C Monteleoni Advances in neural information processing systems 20, 353-360, 2007 | 417 | 2007 |
| A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on learning theory, 33.1-33.34, 2012 | 416 | 2012 |
| Efficient optimal learning for contextual bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang arXiv preprint arXiv:1106.2369, 2011 | 399 | 2011 |
| Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate M Belkin, DJ Hsu, P Mitra Advances in neural information processing systems 31, 2018 | 376 | 2018 |
| A spectral algorithm for latent dirichlet allocation A Anandkumar, Y Liu, D Hsu, DP Foster, SM Kakade Advances in Neural Information Processing Systems, 917-925, 2012 | 366 | 2012 |
| a CAPpella: programming by demonstration of context-aware applications AK Dey, R Hamid, C Beckmann, I Li, D Hsu Proceedings of the SIGCHI conference on Human factors in computing systems …, 2004 | 355 | 2004 |
| Robust Matrix Decomposition with Sparse Corruptions D Hsu, SM Kakade, T Zhang Information Theory, IEEE Transactions on, 1-1, 2011 | 303 | 2011 |
| Discovering unwarranted associations in data-driven applications with the fairtest testing toolkit F Tramèr, V Atlidakis, R Geambasu, DJ Hsu, JP Hubaux, M Humbert, ... CoRR, abs/1510.02377, 2015 | 299* | 2015 |
| Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, D Hsu, SM Kakade, A Rakhlin Advances in Neural Information Processing Systems, 1035-1043, 2011 | 248 | 2011 |
| Loss minimization and parameter estimation with heavy tails D Hsu, S Sabato Journal of Machine Learning Research 17 (18), 1-40, 2016 | 240 | 2016 |
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
