Sham M Kakade

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Daniel HsuColumbia UniversityEmail verificata su cs.columbia.edu
Dean FosterAmazon, NYCEmail verificata su amazon.com
Praneeth NetrapalliGoogle ResearchEmail verificata su google.com
Rong GeDuke UniversityEmail verificata su cs.duke.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAEmail verificata su caltech.edu
Tong ZhangHKUSTEmail verificata su tongzhang-ml.org
John LangfordMicrosoft Research New YorkEmail verificata su hunch.net
Chi JinAssistant Professor, Princeton UniversityEmail verificata su princeton.edu
Prateek JainGoogle Research IndiaEmail verificata su google.com
Aaron SidfordStanford UniversityEmail verificata su stanford.edu
Alekh AgarwalGoogleEmail verificata su google.com
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityEmail verificata su princeton.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinEmail verificata su amazon.de
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganEmail verificata su umich.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichEmail verificata su inf.ethz.ch
Rahul KidambiAmazon SearchEmail verificata su uw.edu
Aravind RajeswaranMeta PlatformsEmail verificata su cs.washington.edu
Akshay KrishnamurthyUniversity of Massachusetts AmherstEmail verificata su cs.umass.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaEmail verificata su cis.upenn.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteEmail verificata su ttic.edu

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Sham M Kakade

Harvard University

Email verificata su seas.harvard.edu - Home page

Machine Learning Artificial Intelligence Statistics Optimization


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	2185	2009
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	1267	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	1217	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	1069	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	946	2002
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	931	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	874	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	794	2017
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	791	2008
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	772	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	772	2011
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003	736	2003
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019	604	2019
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	574	2012
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	565	2000
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	516	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	515	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	468	2018
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang	427	2012
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International Conference on Machine Learning, 1920-1930, 2019	378	2019

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