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
Prateek JainGoogle Research IndiaAdresse e-mail validée de google.com
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
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
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinAdresse e-mail validée de amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganAdresse e-mail validée de umich.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
Rahul KidambiAmazon SearchAdresse e-mail validée de uw.edu
Aravind RajeswaranFacebook AI Research (FAIR), UC BerkeleyAdresse e-mail validée de cs.washington.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaAdresse e-mail validée de cis.upenn.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteAdresse e-mail validée de ttic.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstAdresse e-mail validée de cs.umass.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	2117	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1226	2001
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	1194	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1057	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	931	2002
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	899	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	857	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	776	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	770	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	747	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	747	2011
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	711	2003
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	565	2012
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	559	2019
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	557	2000
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	513	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	507	2002
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	442	2018
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang	418	2012
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	367	2013

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