Sham M Kakade

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Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Praneeth NetrapalliMicrosoft ResearchVerified email at microsoft.com
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Tong ZhangHKUSTVerified email at tongzhang-ml.org
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Chi JinAssistant Professor, Princeton UniversityVerified email at princeton.edu
Aaron SidfordStanford UniversityVerified email at stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Nathaniel D. DawProfessor of Neurocience and Psychology, Princeton UniversityVerified email at princeton.edu
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Rahul KidambiCornell UniversityVerified email at cornell.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Niranjan Srinivas10X GenomicsVerified email at 10xgenomics.com
Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il

Sham M Kakade

University of Washington

Verified email at cs.washington.edu - Homepage

Machine Learning Artificial Intelligence Statistics Optimization


Title Sort by citations Sort by year Sort by title	Cited by 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	1157	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	877	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	851	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	797	2002
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	751	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	645	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	497	2008
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	478	2012
On the sample complexity of reinforcement learning SM Kakade University of London, 2003	470	2003
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	456	2000
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	448	2002
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems, 772-780, 2009	435	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	421	2002
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan arXiv preprint arXiv:1703.00887, 2017	411	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	392	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	392	2011
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	289	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	285	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	272	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	272	2012

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