| Boosting the margin: A new explanation for the effectiveness of voting methods P Bartlett, Y Freund, WS Lee, RE Schapire The annals of statistics 26 (5), 1651-1686, 1998 | 3832 | 1998 |
| New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000 | 3658 | 2000 |
| Learning the kernel matrix with semidefinite programming GRG Lanckriet, N Cristianini, P Bartlett, LE Ghaoui, MI Jordan Journal of Machine learning research 5 (Jan), 27-72, 2004 | 3076 | 2004 |
| Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002 | 2766 | 2002 |
| Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press, 1999 | 2433 | 1999 |
| For valid generalization the size of the weights is more important than the size of the network P Bartlett Advances in neural information processing systems 9, 1996 | 1833 | 1996 |
| A framework for learning predictive structures from multiple tasks and unlabeled data. RK Ando, T Zhang, P Bartlett Journal of Machine Learning Research 6 (11), 2005 | 1689 | 2005 |
| Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006 | 1621 | 2006 |
| Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999 | 1444 | 1999 |
| Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001 | 1183 | 2001 |
| Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017 | 1139 | 2017 |
| Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International Conference on Machine Learning, 5650-5659, 2018 | 1043 | 2018 |
| RL: Fast Reinforcement Learning via Slow Reinforcement Learning Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel arXiv preprint arXiv:1611.02779, 2016 | 1012 | 2016 |
| Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson | 879 | 2005 |
| Structural risk minimization over data-dependent hierarchies J Shawe-Taylor, PL Bartlett, RC Williamson, M Anthony IEEE transactions on Information Theory 44 (5), 1926-1940, 1998 | 723 | 1998 |
| Benign overfitting in linear regression PL Bartlett, PM Long, G Lugosi, A Tsigler Proceedings of the National Academy of Sciences 117 (48), 30063-30070, 2020 | 705 | 2020 |
| Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003 | 586 | 2003 |
| Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004 | 558 | 2004 |
| Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008 | 518 | 2008 |
| Sparse greedy Gaussian process regression A Smola, P Bartlett Advances in neural information processing systems 13, 2000 | 511 | 2000 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeleycs.berkeley.edu의 이메일 확인됨
