Sridhar Mahadevan - Google Scholar

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Mohammad GhavamzadehGoogle ResearchVerified email at inria.fr
Chang WangFMeasure Inc.Verified email at fmeasure.com
Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Melinda DyarMount Holyoke CollegeVerified email at mtholyoke.edu
Georgios TheocharousAdobe ResearchVerified email at adobe.com
Thomas BoucherGritstone OncologyVerified email at cs.umass.edu
Jonathan ConnellIBM T.J. Watson ResearchVerified email at us.ibm.com
Ian GempDeepMindVerified email at google.com
CJ CareyGoogle, LLCVerified email at cs.umass.edu
Jeff JohnsFireEyeVerified email at fireeye.com
Bo LiuAuburn UniversityVerified email at cs.umass.edu
Mauro MaggioniBloomberg Distinguished Professor of Mathematics and Applied MathematicsVerified email at jhu.edu
Kimberly Ferguson-WalterDepartment of DefenseVerified email at spawar.navy.mil
Tom MitchellFounders 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
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
Sarah OsentoskiIron OxVerified email at ironox.com
Marek PetrikUniversity of New HampshireVerified email at cs.unh.edu
Ishan DurugkarGraduate Student, University of Texas at AustinVerified email at cs.utexas.edu

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 Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Recent advances in hierarchical reinforcement learning AG Barto, S Mahadevan Discrete event dynamic systems 13 (1-2), 41-77, 2003	1119	2003
Automatic programming of behavior-based robots using reinforcement learning S Mahadevan, J Connell Artificial intelligence 55 (2-3), 311-365, 1992	862	1992
Average reward reinforcement learning: Foundations, algorithms, and empirical results S Mahadevan Machine learning 22 (1-3), 159-195, 1996	462	1996
LEAP: A learning apprentice for VLSI design TM Mitchell, S Mabadevan, LI Steinberg Machine learning, 271-289, 1990	399	1990
Heterogeneous domain adaptation using manifold alignment C Wang, S Mahadevan IJCAI proceedings-international joint conference on artificial intelligence …, 2011	305	2011
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	291	2007
Robot learning JH Connell, S Mahadevan Springer Science & Business Media, 2012	266	2012
Manifold alignment using procrustes analysis C Wang, S Mahadevan Proceedings of the 25th international conference on Machine learning, 1120-1127, 2008	249	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	225	1999
Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan arXiv preprint arXiv:1611.01673, 2016	202	2016
Manifold alignment without correspondence. C Wang, S Mahadevan IJCAI 2, 3, 2009	175	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	150	1997
Hierarchical multi-agent reinforcement learning R Makar, S Mahadevan, M Ghavamzadeh Proceedings of the fifth international conference on Autonomous agents, 246-253, 2001	145	2001
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	142	2007
Hierarchical multi-agent reinforcement learning M Ghavamzadeh, S Mahadevan, R Makar Autonomous Agents and Multi-Agent Systems 13 (2), 197-229, 2006	135	2006
Supervised actor-critic reinforcement learning MT Rosenstein, AG Barto, J Si, A Barto, W Powell Learning and Approximate Dynamic Programming: Scaling Up to the Real World …, 2004	134	2004
Gaze control for face learning and recognition by humans and machines T Shipley, P Kellman From fragments to objects: Segmentation and grouping in vision 463, 2001	125	2001
Proto-value functions: Developmental reinforcement learning S Mahadevan Proceedings of the 22nd international conference on Machine learning, 553-560, 2005	118	2005
Value function approximation with diffusion wavelets and laplacian eigenfunctions S Mahadevan, M Maggioni Advances in neural information processing systems, 843-850, 2006	110	2006
Hierarchical memory-based reinforcement learning N Hernandez-Gardiol, S Mahadevan Advances in Neural Information Processing Systems, 1047-1053, 2001	110	2001

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