Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerificeret mail på cs.berkeley.edu
Alex SmolaBoson AIVerificeret mail på smola.org
Jonathan BaxterVerificeret mail på baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerificeret mail på tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerificeret mail på comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerificeret mail på umich.edu
John Shawe-TaylorUCLVerificeret mail på cs.ucl.ac.uk
Llew MasonAmazonVerificeret mail på amazon.com
Gabor LugosiICREA and Universitat Pompeu FabraVerificeret mail på upf.edu
Yasin Abbasi YadkoriGoogle DeepMindVerificeret mail på google.com
Alekh AgarwalGoogleVerificeret mail på google.com
Jacob AbernethyAssistant Professor, University of MichiganVerificeret mail på umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerificeret mail på unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyVerificeret mail på stat.berkeley.edu
Marcus FreanVictoria University of WellingtonVerificeret mail på ecs.vuw.ac.nz
Fernando PereiraVP, GoogleVerificeret mail på google.com
Nello CristianiniUniversity of BathVerificeret mail på bath.ac.uk
Gert LanckrietProfessor, UCSDVerificeret mail på eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerificeret mail på stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerificeret mail på princeton.edu

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Verificeret mail på cs.berkeley.edu - Startside

machine learning statistical learning theory adaptive control


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	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	3948	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3780	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	3038	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	1900	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	1817	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
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International conference on machine learning, 5650-5659, 2018	1563	2018
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1561	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	596	2008

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