Sham M Kakade

引用次数

	总计	2019 年至今
引用	38475	26074
h 指数	96	80
i10 指数	186	172

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Daniel HsuColumbia University在 cs.columbia.edu 的电子邮件经过验证
Dean FosterAmazon, NYC在 amazon.com 的电子邮件经过验证
Praneeth NetrapalliGoogle Research在 google.com 的电子邮件经过验证
Rong GeDuke University在 cs.duke.edu 的电子邮件经过验证
Tong ZhangUIUC在 tongzhang-ml.org 的电子邮件经过验证
Anima AnandkumarCalifornia Institute of Technology and NVIDIA在 caltech.edu 的电子邮件经过验证
John LangfordMicrosoft Research New York在 hunch.net 的电子邮件经过验证
Prateek JainGoogle Research India在 google.com 的电子邮件经过验证
Chi JinAssistant Professor, Princeton University在 princeton.edu 的电子邮件经过验证
Alekh AgarwalGoogle在 google.com 的电子邮件经过验证
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton University在 princeton.edu 的电子邮件经过验证
Aaron SidfordStanford University在 stanford.edu 的电子邮件经过验证
Matthias SeegerPrincipal Applied Scientist, Amazon, Berlin在 amazon.de 的电子邮件经过验证
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of Michigan在 umich.edu 的电子邮件经过验证
Akshay KrishnamurthyUniversity of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Andreas KrauseProfessor of Computer Science, ETH Zurich在 inf.ethz.ch 的电子邮件经过验证
Aravind RajeswaranMeta AI, UC Berkeley在 cs.washington.edu 的电子邮件经过验证
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of Washington在 cs.washington.edu 的电子邮件经过验证
Yishay MansourTel Aviv University在 tauex.tau.ac.il 的电子邮件经过验证
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLA在 ee.ucla.edu 的电子邮件经过验证

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Sham M Kakade

Harvard University

在 seas.harvard.edu 的电子邮件经过验证 - 首页

Machine Learning Artificial Intelligence Statistics Optimization


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Gaussian process optimization in the bandit setting: No regret and experimental design N Srinivas, A Krause, SM Kakade, M Seeger arXiv preprint arXiv:0912.3995, 2009	2723	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1580	2001
Tensor decompositions for learning latent variable models. A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky J. Mach. Learn. Res. 15 (1), 2773-2832, 2014	1347	2014
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	1245	2002
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1146	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	1020	2002
Information-theoretic regret bounds for gaussian process optimization in the bandit setting N Srinivas, A Krause, SM Kakade, MW Seeger IEEE Transactions on Information Theory 58 (5), 3250-3265, 2012	1015	2012
Information-Theoretic Regret Boundsfor Gaussian Process Optimization in the Bandit Setting N Srinivas, A Krause, S Kakade, M Seeger Information Theory, IEEE Transactions on, 1-1, 2011	1015	2011
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017	988	2017
Multi-view clustering via canonical correlation analysis K Chaudhuri, SM Kakade, K Livescu, K Sridharan Proceedings of the 26th annual international conference on machine learning …, 2009	955	2009
Stochastic Linear Optimization under Bandit Feedback. V Dani, TP Hayes, SM Kakade COLT 2, 3, 2008	932	2008
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	897	2019
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	839	2003
Global convergence of policy gradient methods for the linear quadratic regulator M Fazel, R Ge, S Kakade, M Mesbahi International conference on machine learning, 1467-1476, 2018	676	2018
Robust aggregation for federated learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022	624	2022
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	617	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	614	2000
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	567	2002
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	528	2009
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International conference on machine learning, 1920-1930, 2019	508	2019

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