Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Jonathan BaxterVerified email at baxters.biz
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Philip LongGoogleVerified email at google.com
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerified email at umich.edu
Llew MasonAmazonVerified email at amazon.com
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Yasin Abbasi YadkoriDeepMindVerified email at google.com
Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Alekh AgarwalGoogleVerified email at google.com
Yoav FreundUCSDVerified email at eng.ucsd.edu
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerified email at unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyVerified email at stat.berkeley.edu
Marcus FreanVictoria University of WellingtonVerified email at ecs.vuw.ac.nz
Nello CristianiniUniversity of BathVerified email at bath.ac.uk
Gert LanckrietProfessor, UCSDVerified email at eng.ucsd.edu

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Verified email at cs.berkeley.edu - Homepage

machine learning statistical learning theory adaptive control


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	3717	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3680	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	3010	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2459	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press, 1999	2201	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	1747	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	1609	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1480	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1308	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	976	2001
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	921	2017
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	838	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	787	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	707	1998
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International Conference on Machine Learning, 5650-5659, 2018	670	2018
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	519	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	505	2004
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	502	2020
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	494	2005
Sparse greedy Gaussian process regression A Smola, P Bartlett Advances in neural information processing systems 13, 2000	482	2000

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