26 citations · 79 across the 16 of their papers we have counts for
17 papers
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
Rachneet Kaur, Nishan Srishankar, Zhen Zeng +2
Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual inte…
TASER: Table Agents for Schema-guided Extraction and Recommendation
Nicole Cho, Kirsty Fielding, William Watson +2
Real-world financial filings report critical information about an entity's investment holdings, essential for assessing that entity's risk, profitability, and relationship profile.…
Scalable Representation Learning for Multimodal Tabular Transactions
Natraj Raman, Sumitra Ganesh, Manuela Veloso
Large language models (LLMs) are primarily designed to understand unstructured text. When directly applied to structured formats such as tabular data, they may struggle to discern…
Learning Payment-Free Resource Allocation Mechanisms
Sihan Zeng, Sujay Bhatt, Eleonora Kreacic +3
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for…
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Yuchen Xiao, Yanchao Sun, Mengda Xu +4
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems. By imitating few-shot examples provided in…
Sequential Fair Resource Allocation under a Markov Decision Process Framework
Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng +2
We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to des…
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Leo Ardon, Alberto Pozanco, Daniel Borrajo +1
Reinforcement Learning (RL) algorithms are known to scale poorly to environments with many available actions, requiring numerous samples to learn an optimal policy. The traditional…
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
Nelson Vadori, Leo Ardon, Sumitra Ganesh +7
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a su…
Phantom -- A RL-driven multi-agent framework to model complex systems
Leo Ardon, Jared Vann, Deepeka Garg +2
Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system an…
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh +4
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained commu…
Mixture of basis for interpretable continual learning with distribution shifts
Mengda Xu, Sumitra Ganesh, Pranay Pasula
Continual learning in environments with shifting data distributions is a challenging problem with several real-world applications. In this paper we consider settings in which the d…
Towards a fully RL-based Market Simulator
Leo Ardon, Nelson Vadori, Thomas Spooner +3
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective…
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Nelson Vadori, Rahul Savani, Thomas Spooner +1
Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whet…
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs
Thomas Spooner, Nelson Vadori, Sumitra Ganesh
Policy gradient methods can solve complex tasks but often fail when the dimensionality of the action-space or objective multiplicity grow very large. This occurs, in part, because…
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games
Nelson Vadori, Sumitra Ganesh, Prashant Reddy +1
Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems. We consider a general sum partially observable…
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty
Nelson Vadori, Sumitra Ganesh, Prashant Reddy +1
We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems. While risk-sensitive formulations for Markov decision proce…
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Sumitra Ganesh, Nelson Vadori, Mengda Xu +3
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In thi…