| Robust estimators in high-dimensions without the computational intractability I Diakonikolas, G Kamath, D Kane, J Li, A Moitra, A Stewart SIAM Journal on Computing 48 (2), 742-864, 2019 | 248 | 2019 |
| Being robust (in high dimensions) can be practical I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart International Conference on Machine Learning, 999-1008, 2017 | 128 | 2017 |
| Sever: A robust meta-algorithm for stochastic optimization I Diakonikolas, G Kamath, D Kane, J Li, J Steinhardt, A Stewart International Conference on Machine Learning, 1596-1606, 2019 | 119 | 2019 |
| Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures I Diakonikolas, DM Kane, A Stewart 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), 73-84, 2017 | 106 | 2017 |
| Robustly learning a gaussian: Getting optimal error, efficiently I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 87 | 2018 |
| Efficient algorithms and lower bounds for robust linear regression I Diakonikolas, W Kong, A Stewart Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 71 | 2019 |
| List-decodable robust mean estimation and learning mixtures of spherical gaussians I Diakonikolas, DM Kane, A Stewart Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing …, 2018 | 69 | 2018 |
| Learning geometric concepts with nasty noise I Diakonikolas, DM Kane, A Stewart Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing …, 2018 | 46 | 2018 |
| Testing bayesian networks CL Canonne, I Diakonikolas, DM Kane, A Stewart Conference on Learning Theory, 370-448, 2017 | 46 | 2017 |
| Robust learning of fixed-structure bayesian networks I Diakonikolas, DM Kane, A Stewart CoRR, abs/1606.07384, 2016 | 40* | 2016 |
| The fourier transform of poisson multinomial distributions and its algorithmic applications I Diakonikolas, DM Kane, A Stewart Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 38 | 2016 |
| Optimal learning via the fourier transform for sums of independent integer random variables I Diakonikolas, DM Kane, A Stewart Conference on Learning Theory, 831-849, 2016 | 33 | 2016 |
| Polynomial time algorithms for multi-type branching processesand stochastic context-free grammars K Etessami, A Stewart, M Yannakakis Proceedings of the forty-fourth annual ACM symposium on Theory of computing …, 2012 | 27 | 2012 |
| Efficient robust proper learning of log-concave distributions I Diakonikolas, DM Kane, A Stewart arXiv preprint arXiv:1606.03077, 2016 | 25 | 2016 |
| Properly learning poisson binomial distributions in almost polynomial time I Diakonikolas, DM Kane, A Stewart Conference on Learning Theory, 850-878, 2016 | 24 | 2016 |
| Testing conditional independence of discrete distributions CL Canonne, I Diakonikolas, DM Kane, A Stewart 2018 Information Theory and Applications Workshop (ITA), 1-57, 2018 | 23 | 2018 |
| Learning multivariate log-concave distributions I Diakonikolas, DM Kane, A Stewart Conference on Learning Theory, 711-727, 2017 | 22 | 2017 |
| Polynomial time algorithms for branching Markov decision processes and probabilistic min (max) polynomial Bellman equations K Etessami, A Stewart, M Yannakakis International Colloquium on Automata, Languages, and Programming, 314-326, 2012 | 19 | 2012 |
| Near-optimal sample complexity bounds for maximum likelihood estimation of multivariate log-concave densities T Carpenter, I Diakonikolas, A Sidiropoulos, A Stewart Conference On Learning Theory, 1234-1262, 2018 | 15 | 2018 |
| Greatest fixed points of probabilistic min/max polynomial equations, and reachability for branching Markov decision processes K Etessami, A Stewart, M Yannakakis Information and Computation 261, 355-382, 2018 | 14 | 2018 |
Ilias DiakonikolasUniversity of Wisconsin-MadisonBestätigte E-Mail-Adresse bei wisc.edu
