Sham M Kakade

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	Toutes	Depuis 2016
Citations	20472	13411
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Coauteurs

Daniel HsuColumbia UniversityAdresse e-mail validée de cs.columbia.edu
Dean FosterAmazon, NYCAdresse e-mail validée de amazon.com
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Praneeth NetrapalliMicrosoft ResearchAdresse e-mail validée de microsoft.com
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
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinAdresse e-mail validée de amazon.de
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganAdresse e-mail validée de umich.edu
Nathaniel D. DawProfessor of Neurocience and Psychology, Princeton UniversityAdresse e-mail validée de princeton.edu
Prateek JainMicrosoft Research IndiaAdresse e-mail validée de microsoft.com
Andreas KrauseProfessor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
Rahul KidambiCornell UniversityAdresse e-mail validée de uw.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteAdresse e-mail validée de ttic.edu
Alekh AgarwalMicrosoft ResearchAdresse e-mail validée de microsoft.com
Michael KearnsProfessor of Computer Science, University of PennsylvaniaAdresse e-mail validée de cis.upenn.edu
Niranjan Srinivas10X GenomicsAdresse e-mail validée de 10xgenomics.com
Aravind RajeswaranUniversity of WashingtonAdresse e-mail validée de cs.washington.edu

Sham M Kakade

University of Washington

Adresse e-mail validée de cs.washington.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	1305	2009
Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky arXiv preprint arXiv:1210.7559, 2012	925	2012
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	908	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	818	2002
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	818	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	704	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	555	2008
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	509	2012
On the sample complexity of reinforcement learning SM Kakade UCL (University College London), 2003	507	2003
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	499	2002
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International Conference on Machine Learning, 1724-1732, 2017	481	2017
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	466	2000
Multi-label prediction via compressed sensing D Hsu, SM Kakade, J Langford, T Zhang arXiv preprint arXiv:0902.1284, 2009	461	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	448	2016
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	434	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	428	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	428	2011
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	316	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	315	2012
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang Electronic Communications in Probability 17, 2012	304	2012

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