Sridhar Mahadevan - Google Scholar Citations

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Mohammad GhavamzadehFacebook AI Research (FAIR)Verified email at inria.fr
Chang WangFMeasure Inc.Verified email at fmeasure.com
Melinda DyarMount Holyoke CollegeVerified email at mtholyoke.edu
Jonathan ConnellIBM T.J. Watson ResearchVerified email at us.ibm.com
Georgios TheocharousAdobe ResearchVerified email at adobe.com
Thomas BoucherGritstone OncologyVerified email at cs.umass.edu
Jeff JohnsFireEyeVerified email at fireeye.com
Ian GempDeepMindVerified email at google.com
CJ CareyGoogle, LLCVerified email at cs.umass.edu
Bo LiuAuburn UniversityVerified email at cs.umass.edu
Mauro MaggioniBloomberg Distinguished Professor of Mathematics and Applied MathematicsVerified email at jhu.edu
Tom MitchellE. Fredkin University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Stephen GiguereUniversity of MassachusettsVerified email at cs.umass.edu
Beverly Park WoolfUniversity of MassachusettsVerified email at cs.umass.edu
Sarah OsentoskiRobert Bosch LLC, Bosch Research and Technology CenterVerified email at us.bosch.com
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
Marek PetrikUniversity of New HampshireVerified email at cs.unh.edu
Ishan DurugkarGraduate Student, University of Texas at AustinVerified email at cs.utexas.edu
Abhijit GosaviAssociate Professor at Missouri S&T (formerly University of Missouri-Rolla)Verified email at mst.edu
Tapas Kumar DasAssociate Professor of Theoretical Astrophysics, HRI Dept of Atomic Energy Govt. of IndiaVerified email at hri.res.in

Sridhar Mahadevan

Director, Data Science Lab, Adobe Research & Professor, University of Massachusetts, Amherst

Verified email at cs.umass.edu - Homepage

Artificial intelligence machine learning


Title	Cited by	Year
Recent advances in hierarchical reinforcement learning AG Barto, S Mahadevan Discrete event dynamic systems 13 (1-2), 41-77, 2003	1113	2003
Automatic programming of behavior-based robots using reinforcement learning S Mahadevan, J Connell Artificial intelligence 55 (2-3), 311-365, 1992	849	1992
Average reward reinforcement learning: Foundations, algorithms, and empirical results S Mahadevan Machine learning 22 (1-3), 159-195, 1996	434	1996
LEAP: A learning apprentice for VLSI design TM Mitchell, S Mabadevan, LI Steinberg Machine learning, 271-289, 1990	403	1990
Multifaceted Therapeutic Benefits of Ginkgo biloba L.: Chemistry, Efficacy, Safety, and Uses S Mahadevan, Y Park Journal of food science 73 (1), R14-R19, 2008	354	2008
Proto-value functions: A Laplacian framework for learning representation and control in Markov decision processes S Mahadevan, M Maggioni Journal of Machine Learning Research 8 (Oct), 2169-2231, 2007	275	2007
Robot learning JH Connell, S Mahadevan Springer Science & Business Media, 2012	264	2012
Heterogeneous domain adaptation using manifold alignment C Wang, S Mahadevan Twenty-Second International Joint Conference on Artificial Intelligence, 2011	264	2011
Manifold alignment using procrustes analysis C Wang, S Mahadevan Proceedings of the 25th international conference on Machine learning, 1120-1127, 2008	232	2008
Solving semi-Markov decision problems using average reward reinforcement learning TK Das, A Gosavi, S Mahadevan, N Marchalleck Management Science 45 (4), 560-574, 1999	214	1999
Manifold alignment without correspondence C Wang, S Mahadevan Twenty-First International Joint Conference on Artificial Intelligence, 2009	155	2009
Self-improving factory simulation using continuous-time average-reward reinforcement learning S Mahadevan, N Marchalleck, TK Das, A Gosavi MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 202-210, 1997	143	1997
Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan arXiv preprint arXiv:1611.01673, 2016	140	2016
Hierarchical multi-agent reinforcement learning R Makar, S Mahadevan, M Ghavamzadeh Proceedings of the fifth international conference on Autonomous agents, 246-253, 2001	138	2001
Hierarchical multi-agent reinforcement learning M Ghavamzadeh, S Mahadevan, R Makar Autonomous Agents and Multi-Agent Systems 13 (2), 197-229, 2006	130	2006
J. 4 supervised actor-critic reinforcement learning M Barto, MT Rosenstein Handbook of learning and approximate dynamic programming 2, 359, 2004	129	2004
Repairing disengagement with non-invasive interventions I Arroyo, K Ferguson, J Johns, T Dragon, H Meheranian, D Fisher, A Barto, ... AIED 2007, 195-202, 2007	127	2007
Gaze control for face learning and recognition by humans and machines TF Shipley, PJ Kellman From fragments to objects: Segmentation and grouping in vision, 463, 2001	124	2001
Proto-value functions: Developmental reinforcement learning S Mahadevan Proceedings of the 22nd international conference on Machine learning, 553-560, 2005	119	2005
Hierarchical memory-based reinforcement learning N Hernandez-Gardiol, S Mahadevan Advances in Neural Information Processing Systems, 1047-1053, 2001	108	2001

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