Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Alex SmolaBoson AIEmail verificata su smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichEmail 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
Philip LongGoogleEmail verificata su google.com
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganEmail verificata su umich.edu
Llew MasonAmazonEmail verificata su amazon.com
John Shawe-TaylorUCLEmail verificata su cs.ucl.ac.uk
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaEmail verificata su cs.ualberta.ca
Yasin Abbasi YadkoriDeepMindEmail verificata su google.com
Gabor LugosiICREA and Universitat Pompeu FabraEmail verificata su upf.edu
Alekh AgarwalGoogleEmail verificata su google.com
Jacob AbernethyAssistant Professor, University of MichiganEmail verificata su umich.edu
Yoav FreundUCSDEmail verificata su eng.ucsd.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneEmail verificata su unimelb.edu.au
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
Nello CristianiniUniversity of BathEmail verificata su bath.ac.uk
Gert LanckrietProfessor, UCSDEmail verificata su eng.ucsd.edu

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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 P Bartlett, Y Freund, WS Lee, RE Schapire The annals of statistics 26 (5), 1651-1686, 1998	3761	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3747	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	3037	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2610	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press, 1999	2291	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	1796	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	1651	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1546	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1360	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	1073	2001
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1037	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	904	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	821	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	803	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	715	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	594	2020
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	548	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	529	2004
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	499	2005
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	489	2008

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