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 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
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Nathaniel D. DawProfessor of Neurocience and Psychology, Princeton UniversityVerified email at princeton.edu
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Rahul KidambiCornell UniversityVerified email at uw.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
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Niranjan Srinivas10X GenomicsVerified email at 10xgenomics.com

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	1342	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	946	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	915	2006
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	839	2001
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	822	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	721	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	572	2008
On the sample complexity of reinforcement learning SM Kakade UCL (University College London), 2003	517	2003
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	515	2002
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	513	2012
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International Conference on Machine Learning, 1724-1732, 2017	492	2017
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	471	2000
Multi-label prediction via compressed sensing D Hsu, SM Kakade, J Langford, T Zhang arXiv preprint arXiv:0902.1284, 2009	463	2009
Learning a predictable and generative vector representation for objects R Girdhar, DF Fouhey, M Rodriguez, A Gupta European Conference on Computer Vision, 484-499, 2016	459	2016
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	442	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	442	2011
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	439	2002
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	318	2013
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	317	2012
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang Electronic Communications in Probability 17, 2012	310	2012

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