Sridhar Mahadevan

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Chang WangFMeasure Inc.Verified email at fmeasure.com
Melinda DyarMount Holyoke CollegeVerified email at mtholyoke.edu
Georgios TheocharousAdobe ResearchVerified email at adobe.com
Thomas BoucherCelsius TherapeuticsVerified email at cs.umass.edu
Ian GempDeepMindVerified email at google.com
Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
CJ CareyGoogleVerified email at google.com
Jonathan ConnellIBM T.J. Watson ResearchVerified email at us.ibm.com
Bo LiuAssociate Professor, Auburn UniversityVerified email at cs.umass.edu
Jeff JohnsFireEyeVerified email at fireeye.com
Mauro MaggioniBloomberg Distinguished Professor of Mathematics, and Applied Mathematics and StatisticsVerified email at jhu.edu
Kimberly Ferguson-WalterDepartment of DefenseVerified email at spawar.navy.mil
Stephen GiguereUniversity of MassachusettsVerified email at cs.umass.edu
Beverly Park WoolfUniversity of MassachusettsVerified email at cs.umass.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
Ishan DurugkarGraduate Student, University of Texas at AustinVerified email at cs.utexas.edu
Louis SteinbergEmeritus Professor of Computer Science, Rutgers UniversityVerified email at cs.rutgers.edu
Marek PetrikUniversity of New HampshireVerified email at cs.unh.edu
Sarah OsentoskiIron OxVerified email at ironox.com

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), 41-77, 2003	1429	2003
Automatic programming of behavior-based robots using reinforcement learning S Mahadevan, J Connell Artificial intelligence 55 (2-3), 311-365, 1992	913	1992
Average reward reinforcement learning: Foundations, algorithms, and empirical results S Mahadevan Machine learning 22 (1), 159-195, 1996	552	1996
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems. E Even-Dar, S Mannor, Y Mansour, S Mahadevan Journal of machine learning research 7 (6), 2006	534	2006
LEAP: A learning apprentice for VLSI design TM Mitchell, S Mabadevan, LI Steinberg Machine learning, 271-289, 1990	422	1990
Heterogeneous domain adaptation using manifold alignment C Wang, S Mahadevan Twenty-second international joint conference on artificial intelligence, 2011	415	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 (10), 2007	339	2007
Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan arXiv preprint arXiv:1611.01673, 2016	320	2016
Manifold alignment using procrustes analysis C Wang, S Mahadevan Proceedings of the 25th international conference on Machine learning, 1120-1127, 2008	302	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	271	1999
Robot learning JH Connell, S Mahadevan Springer Science & Business Media, 2012	263	2012
Manifold alignment without correspondence. C Wang, S Mahadevan IJCAI 2, 3, 2009	212	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	183	1997
Hierarchical multi-agent reinforcement learning R Makar, S Mahadevan, M Ghavamzadeh Proceedings of the fifth international conference on Autonomous agents, 246-253, 2001	169	2001
Hierarchical multi-agent reinforcement learning M Ghavamzadeh, S Mahadevan, R Makar Autonomous Agents and Multi-Agent Systems 13 (2), 197-229, 2006	161	2006
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	154	2007
Proto-value functions: Developmental reinforcement learning S Mahadevan Proceedings of the 22nd international conference on Machine learning, 553-560, 2005	145	2005
Finite-sample analysis of proximal gradient td algorithms B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik arXiv preprint arXiv:2006.14364, 2020	138	2020
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	133	2001
Value function approximation with diffusion wavelets and laplacian eigenfunctions S Mahadevan, M Maggioni Advances in neural information processing systems 18, 2005	121	2005

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