Sham M Kakade

Cited by

	All	Since 2016
Citations	20058	13010
h-index	70	56
i10-index	144	127

4100

2050

1025

3075

200320042005200620072008200920102011201220132014201520162017201820192020202165 74 141 200 208 305 398 439 570 764 940 1249 1375 1684 1852 2378 2948 4034 80

Co-authors

Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Rong GeDuke UniversityVerified email at cs.duke.edu
Praneeth NetrapalliMicrosoft ResearchVerified email at microsoft.com
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
Aaron SidfordStanford UniversityVerified email at stanford.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Nathaniel D. DawProfessor of Neurocience and Psychology, Princeton UniversityVerified email at princeton.edu
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Rahul KidambiCornell UniversityVerified email at cornell.edu
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Niranjan Srinivas10X GenomicsVerified email at 10xgenomics.com
Aravind RajeswaranUniversity of WashingtonVerified email at cs.washington.edu

Sham M Kakade

University of Washington

Verified email at cs.washington.edu - Homepage

Machine Learning Artificial Intelligence Statistics Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	1267	2009
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	914	2014
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006	883	2006
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002	813	2002
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001	813	2001
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	681	2009
Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade	540	2008
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012	504	2012
On the sample complexity of reinforcement learning SM Kakade University of London, 2003	496	2003
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002	495	2002
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan arXiv preprint arXiv:1703.00887, 2017	464	2017
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000	463	2000
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 772-780, 2009	454	2009
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002	432	2002
Learning a predictable and generative vector representation for objects R Girdhar, DF Fouhey, M Rodriguez, A Gupta European Conference on Computer Vision, 484-499, 2016	428	2016
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	422	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	422	2011
A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on Learning Theory, 33.1-33.34, 2012	312	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	307	2013
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, S Kakade, T Zhang Electronic Communications in Probability 17, 2012	293	2012

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Upload PDF

Follow this author

Cited by

Co-authors