765 citations · 786 across the 16 of their papers we have counts for
19 papers
World of Workflows: A Benchmark for Bringing World Models to Enterprise Systems
Lakshya Gupta, Litao Li, Yizhe Liu +5
Frontier large language models (LLMs) excel as autonomous agents in many domains, yet they remain untested in complex enterprise systems where hidden workflows create cascading eff…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
Gustavo Sutter Pessurno de Carvalho, Mohammed Abdulrahman, Hao Wang +7
The optimization of expensive black-box functions is ubiquitous in science and engineering. A common solution to this problem is Bayesian optimization (BO), which is generally comp…
Learning to Negotiate via Voluntary Commitment
Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian +1
The partial alignment and conflict of autonomous agents lead to mixed-motive scenarios in many real-world applications. However, agents may fail to cooperate in practice even when…
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Guiliang Liu, Sheng Xu, Shicheng Liu +3
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints that expert agents adhere to, based on their demonstration data. As an emerging…
Confidence Aware Inverse Constrained Reinforcement Learning
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi +2
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement lea…
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian +3
Bayesian optimization (BO) is an integral part of automated scientific discovery -- the so-called self-driving lab -- where human inputs are ideally minimal or at least non-blockin…
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague +10
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by…
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from…
Decentralized Mean Field Games
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using m…
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley
Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported go…
Multi-Agent Advisor Q-Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and sl…
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
Some reinforcement learning methods suffer from high sample complexity causing them to not be practical in real-world situations. -function reuse, a transfer learning method, is…
Partially Observable Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agen…
Maximum Reward Formulation In Reinforcement Learning
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall +6
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon). However, several cruc…
A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian +3
Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the fi…
Multi Type Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor +1
Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent.…
MPP: Model Performance Predictor
Sindhu Ghanta, Sriram Subramanian, Lior Khermosh +5
Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like ac…
ML Health: Fitness Tracking for Production Models
Sindhu Ghanta, Sriram Subramanian, Lior Khermosh +5
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction qua…