Sham M Kakade

Citirano

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Daniel HsuColumbia UniversityPotvrđena adresa e-pošte na cs.columbia.edu
Dean FosterAmazon, NYCPotvrđena adresa e-pošte na amazon.com
Praneeth NetrapalliGoogle ResearchPotvrđena adresa e-pošte na google.com
Rong GeDuke UniversityPotvrđena adresa e-pošte na cs.duke.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAPotvrđena adresa e-pošte na caltech.edu
Tong ZhangHKUSTPotvrđena adresa e-pošte na tongzhang-ml.org
John LangfordMicrosoft Research New YorkPotvrđena adresa e-pošte na hunch.net
Chi JinAssistant Professor, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Aaron SidfordStanford UniversityPotvrđena adresa e-pošte na stanford.edu
Prateek JainGoogle Research IndiaPotvrđena adresa e-pošte na google.com
Alekh AgarwalGooglePotvrđena adresa e-pošte na google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinPotvrđena adresa e-pošte na amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganPotvrđena adresa e-pošte na umich.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichPotvrđena adresa e-pošte na inf.ethz.ch
Rahul KidambiAmazon SearchPotvrđena adresa e-pošte na uw.edu
Aravind RajeswaranFacebook AI Research (FAIR), UC BerkeleyPotvrđena adresa e-pošte na cs.washington.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaPotvrđena adresa e-pošte na cis.upenn.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstitutePotvrđena adresa e-pošte na ttic.edu
Yishay MansourTel Aviv UniversityPotvrđena adresa e-pošte na tauex.tau.ac.il

Prati

Sham M Kakade

Harvard University

Potvrđena adresa e-pošte na seas.harvard.edu - Početna stranica

Machine Learning Artificial Intelligence Statistics Optimization


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
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	2044	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1196	2001
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	1172	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1041	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	922	2002
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	869	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	847	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	755	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	742	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	719	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	719	2011
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	691	2003
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	559	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	551	2000
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	518	2019
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	508	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	500	2002
Global convergence of policy gradient methods for the linear quadratic regulator M Fazel, R Ge, S Kakade, M Mesbahi International conference on machine learning, 1467-1476, 2018	422	2018
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang	412	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	361	2013

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