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
Jonathan BaxterEmail verificata su baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeEmail verificata su comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganEmail verificata su umich.edu
John Shawe-TaylorUCLEmail verificata su cs.ucl.ac.uk
Llew MasonAmazonEmail verificata su amazon.com
Yasin Abbasi YadkoriGoogle DeepMindEmail 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
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
Fernando PereiraVP, GoogleEmail verificata su google.com
Nello CristianiniUniversity of BathEmail verificata su bath.ac.uk
Gert LanckrietProfessor, UCSDEmail verificata su eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityEmail verificata su stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonEmail verificata su princeton.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	3946	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3777	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	3143	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3028	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press 9, 8, 1999	2619	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	1898	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	1814	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1745	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1555	1999
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International conference on machine learning, 5650-5659, 2018	1546	2018
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1303	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1239	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	1155	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	936	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	899	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	641	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	638	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	614	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	596	2008

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