Sham M Kakade

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Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Praneeth NetrapalliGoogle ResearchVerified email at google.com
Rong GeDuke UniversityVerified email at cs.duke.edu
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
Prateek JainGoogle Research IndiaVerified email at google.com
Aaron SidfordStanford UniversityVerified email at stanford.edu
Alekh AgarwalGoogleVerified email at google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerified email at umich.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Aravind RajeswaranMeta AI, UC BerkeleyVerified email at cs.washington.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAVerified email at ee.ucla.edu

Sham M Kakade

Harvard University

Verified email at seas.harvard.edu - Homepage

Machine Learning Artificial Intelligence Statistics Optimization


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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	2292	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1339	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	1257	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1083	2006
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	993	2002
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	977	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	896	2009
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017	838	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	832	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	832	2011
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	829	2008
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	760	2003
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	669	2019
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	587	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	568	2000
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	530	2002
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	519	2009
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	511	2018
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang	436	2012
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International Conference on Machine Learning, 1920-1930, 2019	414	2019

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