Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的電子郵件地址已通過驗證
Alex SmolaBoson AI在 smola.org 的電子郵件地址已通過驗證
Jonathan Baxter在 baxters.biz 的電子郵件地址已通過驗證
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的電子郵件地址已通過驗證
Wee Sun LeeProfessor, Department of Computer Science, National University of Singapore在 comp.nus.edu.sg 的電子郵件地址已通過驗證
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of Michigan在 umich.edu 的電子郵件地址已通過驗證
John Shawe-TaylorUCL在 cs.ucl.ac.uk 的電子郵件地址已通過驗證
Llew MasonAmazon在 amazon.com 的電子郵件地址已通過驗證
Gabor LugosiICREA and Universitat Pompeu Fabra在 upf.edu 的電子郵件地址已通過驗證
Yasin Abbasi YadkoriGoogle DeepMind在 google.com 的電子郵件地址已通過驗證
Alekh AgarwalGoogle在 google.com 的電子郵件地址已通過驗證
Jacob AbernethyAssistant Professor, University of Michigan在 umich.edu 的電子郵件地址已通過驗證
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of Melbourne在 unimelb.edu.au 的電子郵件地址已通過驗證
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, Berkeley在 stat.berkeley.edu 的電子郵件地址已通過驗證
Marcus FreanVictoria University of Wellington在 ecs.vuw.ac.nz 的電子郵件地址已通過驗證
Fernando PereiraVP, Google在 google.com 的電子郵件地址已通過驗證
Nello CristianiniUniversity of Bath在 bath.ac.uk 的電子郵件地址已通過驗證
Gert LanckrietProfessor, UCSD在 eng.ucsd.edu 的電子郵件地址已通過驗證
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford University在 stanford.edu 的電子郵件地址已通過驗證
Elad HazanProfessor at Princeton University and Director Google AI Princeton在 princeton.edu 的電子郵件地址已通過驗證

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

在 cs.berkeley.edu 的電子郵件地址已通過驗證 - 首頁

machine learning statistical learning theory adaptive control


標題按引用次數排序按年份排序按標題排序	引用次數引用次數	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	3983	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3827	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	3175	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3139	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	2712	2009
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	1926	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	1844	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1796	2006
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International conference on machine learning, 5650-5659, 2018	1787	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1611	1999
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1409	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1264	2001
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	1222	2016
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	988	2020
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	974	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	748	1998
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	693	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	657	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	643	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	623	2008

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