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 SmolaAmazon Web ServicesEmail verificata su smola.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedEmail verificata su tuebingen.mpg.de
Jonathan BaxterEmail verificata su baxters.biz
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeEmail verificata su comp.nus.edu.sg
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganEmail verificata su umich.edu
Llew MasonAmazonEmail verificata su amazon.com
John Shawe-TaylorUCLEmail verificata su cs.ucl.ac.uk
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaEmail verificata su cs.ualberta.ca
Yoav FreundUCSDEmail verificata su eng.ucsd.edu
Philip LongGoogleEmail verificata su google.com
Yasin Abbasi-YadkoriDeepMindEmail verificata su google.com
Alekh AgarwalMicrosoft ResearchEmail verificata su microsoft.com
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of MelbourneEmail verificata su unimelb.edu.au
Gabor LugosiICREA and Universitat Pompeu FabraEmail verificata su upf.edu
Jacob AbernethyAssistant Professor, University of MichiganEmail verificata su umich.edu
Nello CristianiniUniversity of BristolEmail verificata su bristol.ac.uk
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
Gert LanckrietProfessor, UCSDEmail verificata su eng.ucsd.edu

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	3449	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3374	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	2866	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	2006	2002
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1-50, 2000	1940*	2000
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	1820	2009
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	1585*	1998
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	1482	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1299	2006
Boosting algorithms as gradient descent in function space L Mason, J Baxter, P Bartlett, M Frean Nips 11, 512-518, 1999	1004	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	866	2001
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	664	1998
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson The Annals of Statistics 33 (4), 1497-1537, 2005	658	2005
Spectrally-normalized margin bounds for neural networks P Bartlett, DJ Foster, M Telgarsky arXiv preprint arXiv:1706.08498, 2017	564	2017
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	478	2016
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	470	2005
Sparse greedy Gaussian process regression AJ Smola, PL Bartlett Advances in neural information processing systems, 619-625, 2001	442	2001
Model selection and error estimation PL Bartlett, S Boucheron, G Lugosi Machine Learning 48 (1), 85-113, 2002	411	2002
Generalization performance of support vector machines and other pattern classifiers P Bartlett, J Shawe-Taylor Advances in Kernel methods—support vector learning, 43-54, 1999	403	1999
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	395	2004

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