Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerifisert e-postadresse på cs.berkeley.edu
Alex SmolaBoson AIVerifisert e-postadresse på smola.org
Jonathan BaxterVerifisert e-postadresse på baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerifisert e-postadresse på tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerifisert e-postadresse på comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerifisert e-postadresse på umich.edu
Llew MasonAmazonVerifisert e-postadresse på amazon.com
John Shawe-TaylorUCLVerifisert e-postadresse på cs.ucl.ac.uk
Yasin Abbasi YadkoriGoogle DeepMindVerifisert e-postadresse på google.com
Alekh AgarwalGoogleVerifisert e-postadresse på google.com
Gabor LugosiICREA and Universitat Pompeu FabraVerifisert e-postadresse på upf.edu
Jacob AbernethyAssistant Professor, University of MichiganVerifisert e-postadresse på umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneVerifisert e-postadresse på unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyVerifisert e-postadresse på stat.berkeley.edu
Marcus FreanVictoria University of WellingtonVerifisert e-postadresse på ecs.vuw.ac.nz
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerifisert e-postadresse på princeton.edu
Nello CristianiniUniversity of BathVerifisert e-postadresse på bath.ac.uk
Gert LanckrietProfessor, UCSDVerifisert e-postadresse på eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityVerifisert e-postadresse på stanford.edu
Fernando PereiraVP, GoogleVerifisert e-postadresse på google.com

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Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Verifisert e-postadresse på cs.berkeley.edu - Startside

machine learning statistical learning theory adaptive control


Tittel Sorter etter sitater Sorter etter år Sorter etter tittel	Sitert av Sitert av	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	3986	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3848	2000
Rademacher and gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	3197	2002
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	3173	2004
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	2739	2009
Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International conference on machine learning, 5650-5659, 2018	1863	2018
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	1836	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1821	2006
Boosting algorithms as gradient descent L Mason, J Baxter, P Bartlett, M Frean Advances in neural information processing systems 12, 1999	1628	1999
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network PL Bartlett IEEE transactions on Information Theory 44 (2), 525-536, 1998	1573	1998
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1445	2017
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett journal of artificial intelligence research 15, 319-350, 2001	1267	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	1253	2016
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	1038	2020
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson	992	2005
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	759	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	716	2019
Learning rates for Q-learning E Even-Dar, Y Mansour Journal of machine learning Research 5 (Dec), 1-25, 2003	668	2003
Variance reduction techniques for gradient estimates in reinforcement learning E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (Nov), 1471-1530, 2004	646	2004
Classification with a Reject Option using a Hinge Loss. PL Bartlett, MH Wegkamp Journal of Machine Learning Research 9 (8), 2008	630	2008

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