Jayakumar Subramanian
Jayakumar Subramanian
Senior Research Scientist, Adobe India
Verified email at adobe.com
Title
Cited by
Cited by
Year
Reinforcement Learning in Stationary Mean-field Games
J Subramanian, A Mahajan
Proceedings of the 18th International Conference on Autonomous Agents and …, 2019
522019
On the link between weighted least-squares and limiters used in higher-order reconstructions for finite volume computations of hyperbolic equations
JC Mandal, J Subramanian
Applied Numerical Mathematics 58 (5), 705-725, 2008
382008
Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study
S Romero-Brufau, A Chopra, AJ Ryu, E Gel, R Raskar, W Kremers, ...
bmj 373, 2021
292021
Approximate information state for approximate planning and reinforcement learning in partially observed systems
J Subramanian, A Sinha, R Seraj, A Mahajan
arXiv preprint arXiv:2010.08843, 2020
132020
Approximate information state for partially observed systems
J Subramanian, A Mahajan
Reinforcement Learning and Decision Making (RLDM), Montreal, July 7-10, 2019, 2019
132019
Renewal Monte Carlo: Renewal theory based reinforcement learning
J Subramanian, A Mahajan
Proceedings of the IEEE Conference on Decision and Control (CDC), Miami, Florida, 2018
72018
Transient aero-thermal mapping of passive Thermal Protection system for nose-cap of Reusable Hypersonic Vehicle
SP Mahulikar, S Khurana, R Dungarwal, SG Shevakari, J Subramanian, ...
The Journal of the Astronautical Sciences 56 (4), 593-619, 2008
72008
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
TW Killian, H Zhang, J Subramanian, M Fatemi, M Ghassemi
arXiv preprint arXiv:2011.11235, 2020
62020
Stochastic approximation based methods for computing the optimal thresholds in remote-state estimation with packet drops
J Chakravorty, J Subramanian, A Mahajan
American Control Conference (ACC), 2017, 462-467, 2017
62017
High‐resolution finite volume computations using a novel weighted least‐squares formulation
JC Mandal, S Rao, J Subramanian
International journal for numerical methods in fluids 56 (8), 1425-1431, 2008
52008
Reinforcement learning for mean-field teams
J Subramanian, R Seraj, A Mahajan
Reinforcement Learning and Decision Making (RLDM), Montreal, July 7-10, 2019, 2019
42019
Reinforcement Learning for Mean-field Teams
J Subramanian, R Seraj, A Mahajan
AAMAS Workshop on Adaptive and Learning Agents, 2019
42019
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
P Badjatiya, M Sarkar, A Sinha, S Singh, N Puri, J Subramanian, ...
arXiv preprint arXiv:2001.05458, 2020
12020
On controllability of leader-follower dynamics over a directed graph
J Subramanian, A Mahajan
Proceedings of the IEEE Conference on Decision and Control (CDC), Miami, Florida, 2018
12018
Providing insights and suggestions for journeys
P Singhai, P Gupta, B Krishnamurthy, J SUBRAMANIAN, N Puri
US Patent App. 16/910,357, 2021
2021
DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks
A Chopra, E Gel, J Subramanian, B Krishnamurthy, S Romero-Brufau, ...
arXiv preprint arXiv:2110.04421, 2021
2021
Medical Dead-ends and Learning to Identify High-risk States and Treatments
M Fatemi, TW Killian, J Subramanian, M Ghassemi
arXiv preprint arXiv:2110.04186, 2021
2021
Robustness and sample complexity of model-based MARL for general-sum Markov games
J Subramanian, A Sinha, A Mahajan
arXiv preprint arXiv:2110.02355, 2021
2021
Reinforcement Learning in Partially Observed and Multi-agent Systems
J Subramanian
McGill University Libraries, 2020
2020
A policy gradient algorithm to compute boundedly rational stationary mean field equilibria
J Subramanian, A Mahajan
Proceedings of the ICML/IJCAI/AAMAS Workshop on Planning and Learning (PAL …, 2018
2018
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