Peter Bartlett

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

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Email confirmado em cs.berkeley.edu - Página inicial

machine learning statistical learning theory adaptive control


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	3736	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3699	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	3019	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2518	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press, 1999	2238	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	1760	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	1627	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1502	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1337	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	1007	2001
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	962	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	860	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	803	2005
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International Conference on Machine Learning, 5650-5659, 2018	722	2018
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	711	1998
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	542	2020
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	529	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	513	2004
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	495	2005
Sparse greedy Gaussian process regression A Smola, P Bartlett Advances in neural information processing systems 13, 2000	484	2000

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