Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail confirmado em cs.berkeley.edu
Alex SmolaBoson AIEmail confirmado em smola.org
Jonathan BaxterEmail confirmado em baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail confirmado em tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeEmail confirmado em comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganEmail confirmado em umich.edu
John Shawe-TaylorUCLEmail confirmado em cs.ucl.ac.uk
Llew MasonAmazonEmail confirmado em amazon.com
Gabor LugosiICREA and Universitat Pompeu FabraEmail confirmado em upf.edu
Yasin Abbasi YadkoriGoogle DeepMindEmail confirmado em google.com
Alekh AgarwalGoogleEmail confirmado em google.com
Jacob AbernethyAssistant Professor, University of MichiganEmail confirmado em umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneEmail confirmado em unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyEmail confirmado em stat.berkeley.edu
Marcus FreanVictoria University of WellingtonEmail confirmado em ecs.vuw.ac.nz
Fernando PereiraVP, GoogleEmail confirmado em google.com
Nello CristianiniUniversity of BathEmail confirmado em bath.ac.uk
Gert LanckrietProfessor, UCSDEmail confirmado em eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityEmail confirmado em stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonEmail confirmado em princeton.edu

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Email confirmado em cs.berkeley.edu - Página inicial

machine learning statistical learning theory adaptive control


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	3968	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3804	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	3158	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3092	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press 9, 8, 1999	2675	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	1912	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	1834	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1777	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	1673	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1586	1999
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1365	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	1200	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	958	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	951	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	670	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	636	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	609	2008

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