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ORCIDJayakumar Subramanian
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2020 – today
- 2024
[c12]Ayush Chopra, Jayakumar Subramanian, Balaji Krishnamurthy, Ramesh Raskar:
flame: A Framework for Learning in Agent-based ModEls. AAMAS 2024: 391-399- 2023
- [j3]Jayakumar Subramanian
, Amit Sinha, Aditya Mahajan:
Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games. Dyn. Games Appl. 13(1): 56-88 (2023) - [c11]Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar:
Differentiable Agent-based Epidemiology. AAMAS 2023: 1848-1857 - [c10]Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian:
Explaining RL Decisions with Trajectories. ICLR 2023 - [i13]Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian:
Explaining RL Decisions with Trajectories. CoRR abs/2305.04073 (2023) - [i12]Sukriti Verma, Ayush Chopra, Jayakumar Subramanian, Mausoom Sarkar, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy:
SARC: Soft Actor Retrospective Critic. CoRR abs/2306.16503 (2023) - [i11]Shripad Vilasrao Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian, Chirag Agarwal:
Counterfactual Explanation Policies in RL. CoRR abs/2307.13192 (2023) - [i10]Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan:
Mean-field games among teams. CoRR abs/2310.12282 (2023) - [i9]Varun Khurana, Yaman Kumar Singla, Jayakumar Subramanian, Rajiv Ratn Shah, Changyou Chen, Zhiqiang Xu, Balaji Krishnamurthy:
Behavior Optimized Image Generation. CoRR abs/2311.10995 (2023) - 2022
- [j2]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: 12:1-12:83 (2022) - [c9]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: 1530-1532 - [i8]Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar:
Differentiable Agent-based Epidemiology. CoRR abs/2207.09714 (2022) - 2021
- [c8]Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi:
Medical Dead-ends and Learning to Identify High-Risk States and Treatments. NeurIPS 2021: 4856-4870 - [c7]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: 1-12 - [i7]Jayakumar Subramanian, Amit Sinha, Aditya Mahajan:
Robustness and sample complexity of model-based MARL for general-sum Markov games. CoRR abs/2110.02355 (2021) - [i6]Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi:
Medical Dead-ends and Learning to Identify High-risk States and Treatments. CoRR abs/2110.04186 (2021) - [i5]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 abs/2110.04421 (2021) - [i4]Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy:
Status-quo policy gradient in Multi-Agent Reinforcement Learning. CoRR abs/2111.11692 (2021) - 2020
- [j1]Jayakumar Subramanian, Aditya Mahajan:
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning. IEEE Trans. Autom. Control. 65(8): 3663-3670 (2020) - [c6]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: 139-160 - [i3]Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan:
Approximate information state for approximate planning and reinforcement learning in partially observed systems. CoRR abs/2010.08843 (2020) - [i2]Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi:
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. CoRR abs/2011.11235 (2020)
2010 – 2019
- 2019
- [c5]Jayakumar Subramanian, Aditya Mahajan:
Reinforcement Learning in Stationary Mean-field Games. AAMAS 2019: 251-259 - [c4]Jayakumar Subramanian, Aditya Mahajan:
Approximate information state for partially observed systems. CDC 2019: 1629-1636 - 2018
- [c3]Jayakumar Subramanian, Aditya Mahajan, Aditya A. Paranjape:
On Controllability of Leader-Follower Dynamics over a Directed Graph. CDC 2018: 2048-2055 - [c2]Jayakumar Subramanian, Aditya Mahajan:
Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning. CDC 2018: 5759-5764 - [i1]Jayakumar Subramanian, Aditya Mahajan:
Renewal Monte Carlo: Renewal theory based reinforcement learning. CoRR abs/1804.01116 (2018) - 2017
- [c1]Jhelum Chakravorty, Jayakumar Subramanian, Aditya Mahajan:
Stochastic approximation based methods for computing the optimal thresholds in remote-state estimation with packet drops. ACC 2017: 462-467
Coauthor Index
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last updated on 2024-09-13 01:42 CEST by the dblp team
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