Sham M Kakade

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Daniel HsuColumbia UniversityAdresse e-mail validée de cs.columbia.edu
Dean FosterAmazon, NYCAdresse e-mail validée de amazon.com
Praneeth NetrapalliGoogle ResearchAdresse e-mail validée de google.com
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAAdresse e-mail validée de caltech.edu
Tong ZhangHKUSTAdresse e-mail validée de tongzhang-ml.org
John LangfordMicrosoft Research New YorkAdresse e-mail validée de hunch.net
Chi JinAssistant Professor, Princeton UniversityAdresse e-mail validée de princeton.edu
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinAdresse e-mail validée de amazon.de
Prateek JainGoogle Research IndiaAdresse e-mail validée de google.com
Alekh AgarwalGoogleAdresse e-mail validée de google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityAdresse e-mail validée de princeton.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganAdresse e-mail validée de umich.edu
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityAdresse e-mail validée de princeton.edu
Rahul KidambiAmazon SearchAdresse e-mail validée de uw.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
Aravind RajeswaranFacebook AI Research (FAIR), UC BerkeleyAdresse e-mail validée de cs.washington.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteAdresse e-mail validée de ttic.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaAdresse e-mail validée de cis.upenn.edu

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Sham M Kakade

Harvard University

Adresse e-mail validée de seas.harvard.edu - Page d'accueil

Machine Learning Artificial Intelligence Statistics Optimization


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	1973	2009
Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky Journal of machine learning research 15, 2773-2832, 2014	1169	2014
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1155	2001
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1025	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	915	2002
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	839	2009
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	839	2002
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	742	2008
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International Conference on Machine Learning, 1724-1732, 2017	723	2017
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	696	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	696	2011
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	667	2003
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	564	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	540	2000
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	507	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	499	2002
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	487	2019
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang Electronic Communications in Probability 17, 1-6, 2012	403	2012
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	395	2018
Learning mixtures of spherical gaussians: moment methods and spectral decompositions D Hsu, SM Kakade Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013	365	2013

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