Jayakumar Subramanian

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 28

Top Topics

Partially Observed

Stochastic Approximation

Reinforcement Learning

Markov Decision Processes

Top Venues

Publications

Ayush Chopra, Jayakumar Subramanian, Balaji Krishnamurthy, Ramesh Raskar
flame: A Framework for Learning in Agent-based ModEls. AAMAS (2024)
Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian
Explaining RL Decisions with Trajectories. ICLR (2023)
Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian
Explaining RL Decisions with Trajectories. CoRR (2023)
Varun Khurana, Yaman Kumar Singla, Jayakumar Subramanian, Rajiv Ratn Shah, Changyou Chen, Zhiqiang Xu, Balaji Krishnamurthy
Behavior Optimized Image Generation. CoRR (2023)
Shripad Vilasrao Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian, Chirag Agarwal
Counterfactual Explanation Policies in RL. CoRR (2023)
Jayakumar Subramanian, Amit Sinha, Aditya Mahajan
Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games. Dyn. Games Appl. 13 (1) (2023)
Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
Differentiable Agent-based Epidemiology. AAMAS (2023)
Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan
Mean-field games among teams. CoRR (2023)
Sukriti Verma, Ayush Chopra, Jayakumar Subramanian, Mausoom Sarkar, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy
SARC: Soft Actor Retrospective Critic. CoRR (2023)
Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan
Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems. J. Mach. Learn. Res. 23 (2022)
Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
Differentiable Agent-based Epidemiology. CoRR (2022)
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy
Status-quo Policy Gradient in Multi-Agent Reinforcement Learning. AAMAS (2022)
Ayush Chopra, Esma Senturk Gel, Jayakumar Subramanian, Balaji Krishnamurthy, Santiago Romero-Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley, Ramesh Raskar
DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks. CoRR (2021)
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy
Status-quo policy gradient in Multi-Agent Reinforcement Learning. CoRR (2021)
Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
Medical Dead-ends and Learning to Identify High-Risk States and Treatments. NeurIPS (2021)
Ayush Chopra, Ramesh Raskar, Jayakumar Subramanian, Balaji Krishnamurthy, Esma Senturk Gel, Santiago Romero-Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley
DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions. WSC (2021)
Jayakumar Subramanian, Amit Sinha, Aditya Mahajan
Robustness and sample complexity of model-based MARL for general-sum Markov games. CoRR (2021)
Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
Medical Dead-ends and Learning to Identify High-risk States and Treatments. CoRR (2021)
Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan
Approximate information state for approximate planning and reinforcement learning in partially observed systems. CoRR (2020)
Jayakumar Subramanian, Aditya Mahajan
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning. IEEE Trans. Autom. Control. 65 (8) (2020)
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. ML4H@NeurIPS (2020)
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. CoRR (2020)
Jayakumar Subramanian, Aditya Mahajan
Approximate information state for partially observed systems. CDC (2019)
Jayakumar Subramanian, Aditya Mahajan
Reinforcement Learning in Stationary Mean-field Games. AAMAS (2019)
Jayakumar Subramanian, Aditya Mahajan, Aditya A. Paranjape
On Controllability of Leader-Follower Dynamics over a Directed Graph. CDC (2018)
Jayakumar Subramanian, Aditya Mahajan
Renewal Monte Carlo: Renewal theory based reinforcement learning. CoRR (2018)
Jayakumar Subramanian, Aditya Mahajan
Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning. CDC (2018)
Jhelum Chakravorty, Jayakumar Subramanian, Aditya Mahajan
Stochastic approximation based methods for computing the optimal thresholds in remote-state estimation with packet drops. ACC (2017)