Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley確認したメールアドレス: cs.berkeley.edu
Alex SmolaBoson AI確認したメールアドレス: smola.org
Jonathan Baxter確認したメールアドレス: baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH確認したメールアドレス: tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of Singapore確認したメールアドレス: comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of Michigan確認したメールアドレス: umich.edu
John Shawe-TaylorUCL確認したメールアドレス: cs.ucl.ac.uk
Llew MasonAmazon確認したメールアドレス: amazon.com
Gabor LugosiICREA and Universitat Pompeu Fabra確認したメールアドレス: upf.edu
Yasin Abbasi YadkoriGoogle DeepMind確認したメールアドレス: google.com
Alekh AgarwalGoogle確認したメールアドレス: google.com
Jacob AbernethyAssistant Professor, University of Michigan確認したメールアドレス: umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of Melbourne確認したメールアドレス: unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, Berkeley確認したメールアドレス: stat.berkeley.edu
Marcus FreanVictoria University of Wellington確認したメールアドレス: ecs.vuw.ac.nz
Fernando PereiraVP, Google確認したメールアドレス: google.com
Nello CristianiniUniversity of Bath確認したメールアドレス: bath.ac.uk
Gert LanckrietProfessor, UCSD確認したメールアドレス: eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford University確認したメールアドレス: stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI Princeton確認したメールアドレス: princeton.edu

フォロー

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

確認したメールアドレス: cs.berkeley.edu - ホームページ

machine learning statistical learning theory adaptive control


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	3965	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3801	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	3156	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3084	2002
Neural network learning: Theoretical foundations M Anthony, PL Bartlett, PL Bartlett cambridge university press 9, 8, 1999	2672	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	1910	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	1832	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1775	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	1661	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1583	1999
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1360	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1255	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	1194	2016
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	957	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	949	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	746	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	669	2019
Learning Rates for Q-learning. E Even-Dar, Y Mansour, P Bartlett Journal of machine learning Research 5 (1), 2003	652	2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	633	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	608	2008

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