| Recent advances in hierarchical reinforcement learning AG Barto, S Mahadevan Discrete event dynamic systems 13 (4), 341-379, 2003 | 1882 | 2003 |
| Automatic programming of behavior-based robots using reinforcement learning S Mahadevan, J Connell Artificial intelligence 55 (2-3), 311-365, 1992 | 972 | 1992 |
| 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 | 895 | 2006 |
| Average reward reinforcement learning: Foundations, algorithms, and empirical results S Mahadevan Machine learning 22 (1), 159-195, 1996 | 692 | 1996 |
| Heterogeneous domain adaptation using manifold alignment C Wang, S Mahadevan IJCAI proceedings-international joint conference on artificial intelligence …, 2011 | 595 | 2011 |
| Generative multi-adversarial networks I Durugkar, I Gemp, S Mahadevan arXiv preprint arXiv:1611.01673, 2016 | 506 | 2016 |
| LEAP: A learning apprentice for VLSI design TM Mitchell, S Mabadevan, LI Steinberg Machine learning, 271-289, 1990 | 451 | 1990 |
| 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 | 438 | 2007 |
| Manifold alignment using procrustes analysis C Wang, S Mahadevan Proceedings of the 25th international conference on Machine learning, 1120-1127, 2008 | 387 | 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 | 334 | 1999 |
| Robot learning JH Connell, S Mahadevan Springer Science & Business Media, 2012 | 304 | 2012 |
| Manifold alignment without correspondence. C Wang, S Mahadevan IJCAI 2, 3, 2009 | 264 | 2009 |
| Hierarchical multi-agent reinforcement learning R Makar, S Mahadevan, M Ghavamzadeh Proceedings of the fifth international conference on Autonomous agents, 246-253, 2001 | 213 | 2001 |
| Hierarchical multi-agent reinforcement learning M Ghavamzadeh, S Mahadevan, R Makar Autonomous Agents and Multi-Agent Systems 13 (2), 197-229, 2006 | 209 | 2006 |
| 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 | 199 | 1997 |
| A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy TF Boucher, MV Ozanne, ML Carmosino, MD Dyar, S Mahadevan, ... Spectrochimica Acta Part B: Atomic Spectroscopy 107, 1-10, 2015 | 194 | 2015 |
| Proto-value functions: Developmental reinforcement learning S Mahadevan Proceedings of the 22nd international conference on Machine learning, 553-560, 2005 | 194 | 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 | 187 | 2020 |
| 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 | 178 | 2007 |
| 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 | 148 | 2001 |
Chang WangPaper DigestVerified email at paperdigest.org
