no code implementations • 18 Nov 2023 • Varun Khurana, Yaman K Singla, Jayakumar Subramanian, Rajiv Ratn Shah, Changyou Chen, Zhiqiang Xu, Balaji Krishnamurthy
We show that BoigLLM outperforms 13x larger models such as GPT-3. 5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world.
no code implementations • 25 Jul 2023 • Shripad V. Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian, Chirag Agarwal
In this work, we present COUNTERPOL, the first framework to analyze RL policies using counterfactual explanations in the form of minimal changes to the policy that lead to the desired outcome.
1 code implementation • 28 Jun 2023 • Sukriti Verma, Ayush Chopra, Jayakumar Subramanian, Mausoom Sarkar, Nikaash Puri, Piyush Gupta, Balaji Krishnamurthy
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.
1 code implementation • 6 May 2023 • Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian
To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories).
1 code implementation • 20 Jul 2022 • Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments.
no code implementations • 9 Oct 2021 • Ayush Chopra, Esma Gel, Jayakumar Subramanian, Balaji Krishnamurthy, Santiago Romero-Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley, Ramesh Raskar
We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations.
1 code implementation • NeurIPS 2021 • Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning.
1 code implementation • 23 Nov 2020 • Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
1 code implementation • 17 Oct 2020 • Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan
Our key result is to show that if a function of the history (called approximate information state (AIS)) approximately satisfies the properties of the information state, then there is a corresponding approximate dynamic program.
no code implementations • 15 Jan 2020 • Pinkesh Badjatiya, Mausoom Sarkar, Abhishek Sinha, Siddharth Singh, Nikaash Puri, Jayakumar Subramanian, Balaji Krishnamurthy
We show how agents trained with SQLoss evolve cooperative behavior in several social dilemma matrix games.
no code implementations • 3 Apr 2018 • Jayakumar Subramanian, Aditya Mahajan
We generalize the RMC algorithm to post-decision state models and also present a variant that converges faster to an approximately optimal policy.