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 SmolaBoson AIVerified email at smola.org
Jonathan BaxterVerified email at baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerified email at umich.edu
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Llew MasonAmazonVerified email at amazon.com
Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Yasin Abbasi YadkoriGoogle DeepMindVerified email at google.com
Alekh AgarwalGoogleVerified email at google.com
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
Fernando PereiraVP, GoogleVerified email at google.com
Nello CristianiniUniversity of BathVerified email at bath.ac.uk
Gert LanckrietProfessor, UCSDVerified email at eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerified email at stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at princeton.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	3982	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3826	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	3172	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3120	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	2703	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	1924	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	1842	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1790	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	1761	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	1398	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1261	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	1217	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	979	2020
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	969	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	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	685	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	656	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	642	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	618	2008

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