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
4052 1998 New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett
Neural computation 12 (5), 1207-1245, 2000
3976 2000 Rademacher and gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson
Journal of machine learning research 3 (Nov), 463-482, 2002
3574 2002 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
3203 2004 Neural network learning: Theoretical foundations M Anthony, PL Bartlett
cambridge university press, 2009
2976 2009 Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett
International conference on machine learning, 5650-5659, 2018
2507 2018 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
1975 1996 Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe
Journal of the American Statistical Association 101 (473), 138-156, 2006
1945 2006 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
1931 2005 Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean
Advances in neural information processing systems 12, 1999
1720 1999 Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky
Advances in neural information processing systems 30, 2017
1661 2017 RL Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel
arXiv preprint arXiv:1611.02779, 2016
1403 2016 Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett
journal of artificial intelligence research 15, 319-350, 2001
1317 2001 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
1240 2020 Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson
1084 2005 Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks PL Bartlett, N Harvey, C Liaw, A Mehrabian
Journal of Machine Learning Research 20 (63), 1-17, 2019
854 2019 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, 2002
771 2002 Learning rates for Q-learning E Even-Dar, Y Mansour
Journal of machine learning Research 5 (Dec), 1-25, 2003
717 2003 Variance reduction techniques for gradient estimates in reinforcement learning E Greensmith, PL Bartlett, J Baxter
Journal of Machine Learning Research 5 (Nov), 1471-1530, 2004
700 2004 Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp
Journal of Machine Learning Research 9 (8), 2008
678 2008 Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
