Peter Bartlett

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeleycs.berkeley.edu의 이메일 확인됨
Alex SmolaAmazon Web Servicessmola.org의 이메일 확인됨
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and Distinguishedtuebingen.mpg.de의 이메일 확인됨
Jonathan Baxterbaxters.biz의 이메일 확인됨
Wee Sun LeeProfessor, Department of Computer Science, National University of Singaporecomp.nus.edu.sg의 이메일 확인됨
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of Michiganumich.edu의 이메일 확인됨
Llew MasonAmazonamazon.com의 이메일 확인됨
John Shawe-TaylorUCLcs.ucl.ac.uk의 이메일 확인됨
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of Albertacs.ualberta.ca의 이메일 확인됨
Yoav FreundUCSDeng.ucsd.edu의 이메일 확인됨
Philip LongGooglegoogle.com의 이메일 확인됨
Yasin Abbasi-YadkoriDeepMindgoogle.com의 이메일 확인됨
Alekh AgarwalMicrosoft Researchmicrosoft.com의 이메일 확인됨
Benjamin I. P. RubinsteinProfessor, School of Computing and Information Systems, The University of Melbourneunimelb.edu.au의 이메일 확인됨
Gabor LugosiICREA and Universitat Pompeu Fabraupf.edu의 이메일 확인됨
Jacob AbernethyAssistant Professor, University of Michiganumich.edu의 이메일 확인됨
Nello CristianiniUniversity of Bristolbristol.ac.uk의 이메일 확인됨
Jon McAuliffeProfessor (Adj.) of Statistics, University of California, Berkeleystat.berkeley.edu의 이메일 확인됨
Marcus FreanVictoria University of Wellingtonecs.vuw.ac.nz의 이메일 확인됨
Gert LanckrietProfessor, UCSDeng.ucsd.edu의 이메일 확인됨

Peter Bartlett

Professor, EECS and Statistics, UC Berkeley

cs.berkeley.edu의 이메일 확인됨 - 홈페이지

machine learning statistical learning theory adaptive control


제목 서지정보순 정렬 연도순 정렬 제목순 정렬	인용 인용	연도
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	3431	1998
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3358	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	2861	2004
Rademacher and Gaussian complexities: Risk bounds and structural results PL Bartlett, S Mendelson Journal of Machine Learning Research 3 (Nov), 463-482, 2002	1988	2002
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13 (1), 1-50, 2000	1932*	2000
Neural network learning: Theoretical foundations M Anthony, PL Bartlett cambridge university press, 2009	1801	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	1574*	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	1477	2005
Convexity, classification, and risk bounds PL Bartlett, MI Jordan, JD McAuliffe Journal of the American Statistical Association 101 (473), 138-156, 2006	1291	2006
Boosting algorithms as gradient descent in function space L Mason, J Baxter, P Bartlett, M Frean Nips 11, 512-518, 1999	992	1999
Infinite-horizon policy-gradient estimation J Baxter, PL Bartlett Journal of Artificial Intelligence Research 15, 319-350, 2001	862	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	662	1998
Local rademacher complexities PL Bartlett, O Bousquet, S Mendelson The Annals of Statistics 33 (4), 1497-1537, 2005	648	2005
Spectrally-normalized margin bounds for neural networks P Bartlett, DJ Foster, M Telgarsky arXiv preprint arXiv:1706.08498, 2017	552	2017
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil, P Bartlett Journal of machine learning research 6 (7), 2005	467	2005
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	459	2016
Sparse greedy Gaussian process regression AJ Smola, PL Bartlett Advances in neural information processing systems, 619-625, 2001	440	2001
Model selection and error estimation PL Bartlett, S Boucheron, G Lugosi Machine Learning 48 (1), 85-113, 2002	409	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	399	1999
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. E Greensmith, PL Bartlett, J Baxter Journal of Machine Learning Research 5 (9), 2004	392	2004

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