Sham M Kakade

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Daniel HsuColumbia UniversityPreverjeni e-poštni naslov na cs.columbia.edu
Dean FosterAmazon, NYCPreverjeni e-poštni naslov na amazon.com
Praneeth NetrapalliGoogle ResearchPreverjeni e-poštni naslov na google.com
Rong GeDuke UniversityPreverjeni e-poštni naslov na cs.duke.edu
Tong ZhangUIUCPreverjeni e-poštni naslov na tongzhang-ml.org
Anima AnandkumarCalifornia Institute of Technology and NVIDIAPreverjeni e-poštni naslov na caltech.edu
John LangfordMicrosoft Research New YorkPreverjeni e-poštni naslov na hunch.net
Prateek JainGoogle Research IndiaPreverjeni e-poštni naslov na google.com
Chi JinAssistant Professor, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Alekh AgarwalGooglePreverjeni e-poštni naslov na google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Aaron SidfordStanford UniversityPreverjeni e-poštni naslov na stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinPreverjeni e-poštni naslov na amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganPreverjeni e-poštni naslov na umich.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstPreverjeni e-poštni naslov na cs.umass.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichPreverjeni e-poštni naslov na inf.ethz.ch
Aravind RajeswaranMeta AI, UC BerkeleyPreverjeni e-poštni naslov na cs.washington.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPreverjeni e-poštni naslov na cs.washington.edu
Yishay MansourTel Aviv UniversityPreverjeni e-poštni naslov na tauex.tau.ac.il
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAPreverjeni e-poštni naslov na ee.ucla.edu

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Sham M Kakade

Harvard University

Preverjeni e-poštni naslov na seas.harvard.edu - Domača stran

Machine Learning Artificial Intelligence Statistics Optimization


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	2736	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1583	2001
Tensor decompositions for learning latent variable models. A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky J. Mach. Learn. Res. 15 (1), 2773-2832, 2014	1352	2014
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	1249	2002
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1147	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	1025	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	1020	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	1020	2011
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017	994	2017
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	956	2009
Stochastic Linear Optimization under Bandit Feedback. V Dani, TP Hayes, SM Kakade COLT 2, 3, 2008	936	2008
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	903	2019
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	838	2003
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	682	2018
Robust aggregation for federated learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022	631	2022
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	618	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	616	2000
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	568	2002
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	529	2009
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International conference on machine learning, 1920-1930, 2019	508	2019

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