Sham M Kakade - Google Scholar Citations

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Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Rong GeDuke UniversityVerified email at cs.duke.edu
Anima AnandkumarCalifornia Institute of TechnologyVerified email at caltech.edu
Praneeth NetrapalliMicrosoft ResearchVerified email at microsoft.com
Tong ZhangHKUSTVerified email at tongzhang-ml.org
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Aaron SidfordStanford UniversityVerified email at stanford.edu
Chi JinPh.D student, UC BerkeleyVerified email at berkeley.edu
Nathaniel D. DawProfessor of Neurocience and Psychology, Princeton UniversityVerified email at princeton.edu
Ambuj Tewari (अम्बुज तिवारी)Associate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Rahul KidambiCornell UniversityVerified email at cornell.edu
Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Varsha DaniUniversity of New MexicoVerified email at cs.unm.edu
Matus TelgarskyUniversity of Illinois, Urbana-ChampaignVerified email at illinois.edu

Sham M Kakade

University of Washington

Verified email at cs.washington.edu - Homepage

Machine Learning Artificial Intelligence Statistics Optimization


Title	Cited by	Year
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	846	2009
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	781	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural Networks 15 (4-6), 603-616, 2002	735	2002
Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky The Journal of Machine Learning Research 15 (1), 2773-2832, 2014	731	2014
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	599	2001
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	560	2009
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	431	2012
Learning and selective attention P Dayan, S Kakade, PR Montague nature neuroscience 3 (11s), 1218, 2000	425	2000
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems, 772-780, 2009	396	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	391	2008
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	384	2002
On the sample complexity of reinforcement learning SM Kakade University of London, 2003	373	2003
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	303	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	291	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	291	2011
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	277	2017
A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on Learning Theory, 33.1-33.34, 2012	259	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	256	2013
A spectral algorithm for latent dirichlet allocation A Anandkumar, DP Foster, DJ Hsu, SM Kakade, YK Liu Advances in Neural Information Processing Systems, 917-925, 2012	243	2012
A spectral algorithm for latent dirichlet allocation A Anandkumar, DP Foster, DJ Hsu, SM Kakade, YK Liu Advances in Neural Information Processing Systems, 917-925, 2012	243	2012

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