Peter Bartlett - Google Scholar

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Citazioni	34836	14620
Indice H	69	52
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Alex SmolaAmazon Web ServicesEmail verificata su smola.org
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedEmail verificata su tuebingen.mpg.de
Jonathan BaxterEmail verificata su baxters.biz
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeEmail verificata su comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganEmail verificata su umich.edu
Llew MasonAmazonEmail verificata su amazon.com
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaEmail verificata su cs.ualberta.ca
John Shawe-TaylorUCLEmail verificata su cs.ucl.ac.uk
Alekh AgarwalMicrosoft ResearchEmail verificata su microsoft.com
Yasin Abbasi-YadkoriDeepMindEmail verificata su google.com
Benjamin I. P. RubinsteinAssociate Professor, School of Computing and Information Systems, The University of MelbourneEmail verificata su unimelb.edu.au
Jacob AbernethyAssistant Professor, University of MichiganEmail verificata su umich.edu
Gabor LugosiICREA and Universitat Pompeu FabraEmail verificata su upf.edu
Nello CristianiniUniversity of BristolEmail verificata su bristol.ac.uk
Philip LongGoogleEmail verificata su google.com
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyEmail verificata su stat.berkeley.edu
Marcus FreanVictoria University of WellingtonEmail verificata su ecs.vuw.ac.nz
Yoav FreundUCSDEmail verificata su eng.ucsd.edu
Gert LanckrietProfessor, UCSDEmail verificata su eng.ucsd.edu

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Email verificata su cs.berkeley.edu - Home page

machine learning statistical learning theory adaptive control


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	3264	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3177	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	2728	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	1721	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	1641	2009
For valid generalization the size of the weights is more important than the size of the network PL Bartlett Advances in neural information processing systems, 134-140, 1997	1471	1997
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1, 2000	1452	2000
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1175	2006
Boosting algorithms as gradient descent L Mason, J Baxter, PL Bartlett, MR Frean Advances in neural information processing systems, 512-518, 2000	861	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	812	2011
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	805	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	637	1998
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson The Annals of Statistics 33 (4), 1497-1537, 2005	562	2005
Learning the kernel function via regularization CA Micchelli, M Pontil Journal of machine learning research 6 (Jul), 1099-1125, 2005	440	2005
Generalized support vector machines O Mangasarian	423	1998
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in Neural Information Processing Systems, 6240-6249, 2017	409	2017
Sparse greedy Gaussian process regression AJ Smola, PL Bartlett Advances in neural information processing systems, 619-625, 2001	393	2001
Model selection and error estimation PL Bartlett, S Boucheron, G Lugosi Machine Learning 48 (1-3), 85-113, 2002	386	2002
Advances in large margin classifiers AJ Smola, PJ Bartlett, D Schuurmans, B Schölkopf MIT press, 2000	383	2000
A comment on D PL Bartlett, MI Jordan, JD Mcauliffe V. Lindley’s statistical paradox, Biometrika, 1957	377	1957

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