Peter Bartlett

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	전체	2019년 이후
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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeleycs.berkeley.edu의 이메일 확인됨
Alex SmolaBoson AIsmola.org의 이메일 확인됨
Jonathan Baxterbaxters.biz의 이메일 확인됨
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHtuebingen.mpg.de의 이메일 확인됨
Wee Sun LeeProfessor, Department of Computer Science, National University of Singaporecomp.nus.edu.sg의 이메일 확인됨
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of Michiganumich.edu의 이메일 확인됨
John Shawe-TaylorUCLcs.ucl.ac.uk의 이메일 확인됨
Llew MasonAmazonamazon.com의 이메일 확인됨
Gabor LugosiICREA and Universitat Pompeu Fabraupf.edu의 이메일 확인됨
Yasin Abbasi YadkoriGoogle DeepMindgoogle.com의 이메일 확인됨
Alekh AgarwalGooglegoogle.com의 이메일 확인됨
Jacob AbernethyAssistant Professor, University of Michiganumich.edu의 이메일 확인됨
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of Melbourneunimelb.edu.au의 이메일 확인됨
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, Berkeleystat.berkeley.edu의 이메일 확인됨
Marcus FreanVictoria University of Wellingtonecs.vuw.ac.nz의 이메일 확인됨
Fernando PereiraVP, Googlegoogle.com의 이메일 확인됨
Nello CristianiniUniversity of Bathbath.ac.uk의 이메일 확인됨
Gert LanckrietProfessor, UCSDeng.ucsd.edu의 이메일 확인됨
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford Universitystanford.edu의 이메일 확인됨
Elad HazanProfessor at Princeton University and Director Google AI Princetonprinceton.edu의 이메일 확인됨

팔로우

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

cs.berkeley.edu의 이메일 확인됨 - 홈페이지

machine learning statistical learning theory adaptive control


제목 서지정보순 정렬 연도순 정렬 제목순 정렬	인용 인용	연도
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	3965	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3801	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	3156	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3084	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press 9, 8, 1999	2672	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	1910	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	1832	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1775	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	1661	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1583	1999
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1360	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1255	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	1194	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	957	2005
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	949	2020
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	746	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	669	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	652	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	633	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	608	2008

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