23 citations · 42 across the 7 of their papers we have counts for
7 papers
In-Context Learning with Topological Information for Knowledge Graph Completion
Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann +1
Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answe…
A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis
Deepeka Garg, Benjamin Patrick Evans, Leo Ardon +5
Mortgages account for the largest portion of household debt in the United States, totaling around $12 trillion nationwide. In times of financial hardship, alleviating mortgage bur…
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…
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…
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…
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets
Selim Amrouni, Aymeric Moulin, Jared Vann +3
Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs…