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
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
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
Yoav FreundUCSDVerified email at eng.ucsd.edu
Philip LongGoogleVerified email at google.com
Yasin Abbasi-YadkoriDeepMindVerified email at google.com
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerified email at unimelb.edu.au
Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.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
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	3480	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3397	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	2880	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2035	2002
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1-50, 2000	1950*	2000
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	1848	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	1607*	1998
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	1489	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1320	2006
Boosting algorithms as gradient descent in function space L Mason, J Baxter, P Bartlett, M Frean Nips 11, 512-518, 1999	1024	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	871	2001
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson The Annals of Statistics 33 (4), 1497-1537, 2005	666	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	666	1998
Spectrally-normalized margin bounds for neural networks P Bartlett, DJ Foster, M Telgarsky arXiv preprint arXiv:1706.08498, 2017	588	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	497	2016
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	471	2005
Sparse greedy Gaussian process regression AJ Smola, PL Bartlett Advances in neural information processing systems, 619-625, 2001	443	2001
Model selection and error estimation PL Bartlett, S Boucheron, G Lugosi Machine Learning 48 (1), 85-113, 2002	412	2002
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	406	2004
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	403	1999

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