| Contextual bandits with linear payoff functions W Chu, L Li, L Reyzin, RE Schapire International Conference on Artificial Intelligence and Statistics, 2011 | 1251 | 2011 |
| Efficient optimal learning for contextual bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang Conference on Uncertainty in Artificial Intelligence, 2011 | 367 | 2011 |
| Contextual bandit algorithms with supervised learning guarantees A Beygelzimer, J Langford, L Li, L Reyzin, RE Schapire International Conference on Artificial Intelligence and Statistics, 2011 | 364 | 2011 |
| How boosting the margin can also boost classifier complexity L Reyzin, RE Schapire International Conference on Machine Learning, 2006 | 297 | 2006 |
| Statistical algorithms and a lower bound for detecting planted cliques V Feldman, E Grigorescu, L Reyzin, SS Vempala, Y Xiao Journal of the ACM, 2017 | 291 | 2017 |
| Non-stochastic bandit slate problems S Kale, L Reyzin, RE Schapire Neural Information Processing Systems, 2010 | 101 | 2010 |
| Learning and verifying graphs using queries with a focus on edge counting L Reyzin, N Srivastava International Conference on Algorithmic Learning Theory, 2007 | 71 | 2007 |
| Network construction with subgraph connectivity constraints D Angluin, J Aspnes, L Reyzin Journal of Combinatorial Optimization, 2015 | 66* | 2015 |
| Data stability in clustering: a closer look S Ben-David, L Reyzin Theoretical Computer Science, 2014 | 52* | 2014 |
| Anti-coordination games and stable graph colorings J Kun, B Powers, L Reyzin Symposium on Algorithmic Game Theory, 2013 | 44 | 2013 |
| On the longest path algorithm for reconstructing trees from distance matrices L Reyzin, N Srivastava Information processing letters, 2007 | 42 | 2007 |
| Boosting on a budget: sampling for feature-efficient prediction L Reyzin International Conference on Machine Learning, 2011 | 38* | 2011 |
| Statistical queries and statistical algorithms: foundations and applications L Reyzin arXiv preprint arXiv:2004.00557, 2020 | 34 | 2020 |
| On the computational complexity of MapReduce B Fish, J Kun, ÁD Lelkes, L Reyzin, G Turán Symposium on Distributed Computing, 2015 | 32 | 2015 |
| On noise-tolerant learning of sparse parities and related problems E Grigorescu, L Reyzin, S Vempala International Conference on Algorithmic Learning Theory, 2011 | 30 | 2011 |
| Unprovability comes to machine learning L Reyzin Nature, 2019 | 27 | 2019 |
| Shift-pessimistic active learning using robust bias-aware prediction A Liu, L Reyzin, BD Ziebart AAAI Conference on Artificial Intelligence, 2015 | 26* | 2015 |
| On the complexity of learning a class ratio from unlabeled data B Fish, L Reyzin Journal of Artificial Intelligence Research, 2020 | 21* | 2020 |
| Improved algorithms for distributed boosting J Cooper, L Reyzin Allerton Conference on Communication, Control, and Computing, 2017 | 20 | 2017 |
| Learning large-alphabet and analog circuits with value injection queries D Angluin, J Aspnes, J Chen, L Reyzin Machine Learning, 2008 | 20 | 2008 |
Robert SchapireMicrosoft Research在 microsoft.com 的电子邮件经过验证
