Sham M Kakade

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Daniel HsuColumbia UniversityDirección de correo verificada de cs.columbia.edu
Dean FosterAmazon, NYCDirección de correo verificada de amazon.com
Praneeth NetrapalliGoogle ResearchDirección de correo verificada de google.com
Rong GeDuke UniversityDirección de correo verificada de cs.duke.edu
Tong ZhangUIUCDirección de correo verificada de tongzhang-ml.org
Anima AnandkumarCalifornia Institute of Technology and NVIDIADirección de correo verificada de caltech.edu
John LangfordMicrosoft Research New YorkDirección de correo verificada de hunch.net
Prateek JainGoogle Research IndiaDirección de correo verificada de google.com
Chi JinAssistant Professor, Princeton UniversityDirección de correo verificada de princeton.edu
Alekh AgarwalGoogleDirección de correo verificada de google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityDirección de correo verificada de princeton.edu
Aaron SidfordStanford UniversityDirección de correo verificada de stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinDirección de correo verificada de amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganDirección de correo verificada de umich.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichDirección de correo verificada de inf.ethz.ch
Aravind RajeswaranMeta AI, UC BerkeleyDirección de correo verificada de cs.washington.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonDirección de correo verificada de cs.washington.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLADirección de correo verificada de ee.ucla.edu
Yishay MansourTel Aviv UniversityDirección de correo verificada de tauex.tau.ac.il

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Sham M Kakade

Harvard University

Dirección de correo verificada de seas.harvard.edu - Página principal

Machine Learning Artificial Intelligence Statistics Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	2894	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1648	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	1381	2014
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	1321	2002
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1176	2006
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	1080	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	1080	2011
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	1039	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	1031	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	970	2009
Stochastic Linear Optimization under Bandit Feedback. V Dani, TP Hayes, SM Kakade COLT 2, 3, 2008	970	2008
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	966	2019
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	870	2003
Robust aggregation for federated learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022	734	2022
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	720	2018
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	629	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	622	2000
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	580	2002
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International conference on machine learning, 1920-1930, 2019	545	2019
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	533	2009

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