Sham M Kakade

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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
Praneeth NetrapalliGoogle ResearchVerified email at google.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
Alekh AgarwalGoogleVerified email at google.com
Ambuj Tewari | अम्बुज तिवारीProfessor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Prateek JainGoogle Research IndiaVerified email at google.com
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityVerified email at princeton.edu
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
Niranjan Srinivas10X GenomicsVerified email at 10xgenomics.com
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Aravind RajeswaranFacebook AI Research (FAIR), UC BerkeleyVerified email at cs.washington.edu

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	1610	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	1048	2014
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	983	2001
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	965	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	860	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	765	2009
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	656	2002
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	644	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	591	2017
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	585	2003
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	537	2012
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	535	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	535	2011
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	500	2000
Multi-label prediction via compressed sensing D Hsu, SM Kakade, J Langford, T Zhang arXiv preprint arXiv:0902.1284, 2009	485	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	456	2002
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang Electronic Communications in Probability 17, 1-6, 2012	355	2012
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	338	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	335	2013
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization SM Kakade, K Sridharan, A Tewari	317	2008

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