Peter Bartlett

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	总计	2018 年至今
引用	48490	23465
h 指数	83	59
i10 指数	226	167

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Alex SmolaBoson AI在 smola.org 的电子邮件经过验证
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的电子邮件经过验证
Jonathan Baxter在 baxters.biz 的电子邮件经过验证
Philip LongGoogle在 google.com 的电子邮件经过验证
Wee Sun LeeProfessor, Department of Computer Science, National University of Singapore在 comp.nus.edu.sg 的电子邮件经过验证
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of Michigan在 umich.edu 的电子邮件经过验证
John Shawe-TaylorUCL在 cs.ucl.ac.uk 的电子邮件经过验证
Llew MasonAmazon在 amazon.com 的电子邮件经过验证
Yasin Abbasi YadkoriDeepMind在 google.com 的电子邮件经过验证
Gabor LugosiICREA and Universitat Pompeu Fabra在 upf.edu 的电子邮件经过验证
Alekh AgarwalGoogle在 google.com 的电子邮件经过验证
Jacob AbernethyAssistant Professor, University of Michigan在 umich.edu 的电子邮件经过验证
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of Melbourne在 unimelb.edu.au 的电子邮件经过验证
Yoav FreundUCSD在 eng.ucsd.edu 的电子邮件经过验证
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, Berkeley在 stat.berkeley.edu 的电子邮件经过验证
Marcus FreanVictoria University of Wellington在 ecs.vuw.ac.nz 的电子邮件经过验证
Nello CristianiniUniversity of Bath在 bath.ac.uk 的电子邮件经过验证
Gert LanckrietProfessor, UCSD在 eng.ucsd.edu 的电子邮件经过验证
Fernando PereiraVP, Google在 google.com 的电子邮件经过验证

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

在 cs.berkeley.edu 的电子邮件经过验证 - 首页

machine learning statistical learning theory adaptive control


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	3830	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3654	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	3071	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2761	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press, 1999	2430	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	1688	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1620	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1437	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1179	2001
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1137	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	1040	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	1009	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	878	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	724	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	701	2020
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	582	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	554	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	516	2008
Sparse greedy Gaussian process regression A Smola, P Bartlett Advances in neural information processing systems 13, 2000	509	2000

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