Peter Bartlett

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	All	Since 2016
Citations	37304	14777
h-index	72	53
i10-index	194	131

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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
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of 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
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Yasin Abbasi-YadkoriDeepMindVerified email at google.com
Philip LongGoogleVerified email at google.com
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerified email at unimelb.edu.au
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Nello CristianiniUniversity of BristolVerified email at bristol.ac.uk
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
Yoav FreundUCSDVerified email at eng.ucsd.edu
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 RE Schapire, Y Freund, P Bartlett, WS Lee The annals of statistics 26 (5), 1651-1686, 1998	3373	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3287	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	2819	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	1911	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	1752	2009
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network PL Bartlett IEEE transactions on Information Theory 44 (2), 525-536, 1998	1535	1998
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1, 2000	1487	2000
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1261	2006
Boosting algorithms as gradient descent L Mason, J Baxter, PL Bartlett, MR Frean Advances in neural information processing systems, 512-518, 2000	954	2000
FaST linear mixed models for genome-wide association studies C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman Nature methods 8 (10), 833-835, 2011	867	2011
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	860	2001
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	656	1998
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson The Annals of Statistics 33 (4), 1497-1537, 2005	630	2005
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems, 6240-6249, 2017	503	2017
Learning the kernel function via regularization CA Micchelli, M Pontil Journal of machine learning research 6 (Jul), 1099-1125, 2005	457	2005
Generalized support vector machines O Mangasarian	433	1998
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	431	2016
Sparse greedy Gaussian process regression AJ Smola, PL Bartlett Advances in neural information processing systems, 619-625, 2001	422	2001
Model selection and error estimation PL Bartlett, S Boucheron, G Lugosi Machine Learning 48 (1-3), 85-113, 2002	401	2002
Generalization performance of support vector machines and other pattern classifiers P Bartlett, J Shawe-Taylor Advances in Kernel methods—support vector learning, 43-54, 1999	395	1999

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