Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerifierad e-postadress på cs.berkeley.edu
Alex SmolaBoson AIVerifierad e-postadress på smola.org
Jonathan BaxterVerifierad e-postadress på baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerifierad e-postadress på tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerifierad e-postadress på comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerifierad e-postadress på umich.edu
John Shawe-TaylorUCLVerifierad e-postadress på cs.ucl.ac.uk
Llew MasonAmazonVerifierad e-postadress på amazon.com
Gabor LugosiICREA and Universitat Pompeu FabraVerifierad e-postadress på upf.edu
Yasin Abbasi YadkoriGoogle DeepMindVerifierad e-postadress på google.com
Alekh AgarwalGoogleVerifierad e-postadress på google.com
Jacob AbernethyAssistant Professor, University of MichiganVerifierad e-postadress på umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerifierad e-postadress på unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyVerifierad e-postadress på stat.berkeley.edu
Marcus FreanVictoria University of WellingtonVerifierad e-postadress på ecs.vuw.ac.nz
Fernando PereiraVP, GoogleVerifierad e-postadress på google.com
Nello CristianiniUniversity of BathVerifierad e-postadress på bath.ac.uk
Gert LanckrietProfessor, UCSDVerifierad e-postadress på eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerifierad e-postadress på stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerifierad e-postadress på princeton.edu

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Verifierad e-postadress på cs.berkeley.edu - Startsida

machine learning statistical learning theory adaptive control


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	3950	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3781	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	3144	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3039	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press 9, 8, 1999	2620	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	1901	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	1818	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1746	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	1565	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1562	1999
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1310	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1240	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	1159	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	937	2005
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	910	2020
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	744	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	646	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	641	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	617	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	597	2008

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