Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPreverjeni e-poštni naslov na cs.berkeley.edu
Alex SmolaBoson AIPreverjeni e-poštni naslov na smola.org
Jonathan BaxterPreverjeni e-poštni naslov na baxters.biz
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHPreverjeni e-poštni naslov na tuebingen.mpg.de
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporePreverjeni e-poštni naslov na comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganPreverjeni e-poštni naslov na umich.edu
John Shawe-TaylorUCLPreverjeni e-poštni naslov na cs.ucl.ac.uk
Llew MasonAmazonPreverjeni e-poštni naslov na amazon.com
Yasin Abbasi YadkoriGoogle DeepMindPreverjeni e-poštni naslov na google.com
Gabor LugosiICREA and Universitat Pompeu FabraPreverjeni e-poštni naslov na upf.edu
Alekh AgarwalGooglePreverjeni e-poštni naslov na google.com
Jacob AbernethyAssistant Professor, University of MichiganPreverjeni e-poštni naslov na umich.edu
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbournePreverjeni e-poštni naslov na unimelb.edu.au
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, BerkeleyPreverjeni e-poštni naslov na stat.berkeley.edu
Marcus FreanVictoria University of WellingtonPreverjeni e-poštni naslov na ecs.vuw.ac.nz
Fernando PereiraVP, GooglePreverjeni e-poštni naslov na google.com
Nello CristianiniUniversity of BathPreverjeni e-poštni naslov na bath.ac.uk
Gert LanckrietProfessor, UCSDPreverjeni e-poštni naslov na eng.ucsd.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonPreverjeni e-poštni naslov na princeton.edu

Spremljaj

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

Preverjeni e-poštni naslov na cs.berkeley.edu - Domača stran

machine learning statistical learning theory adaptive control


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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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