26 citations · 71 across the 11 of their papers we have counts for
12 papers
Learning Mathematical Rules with Large Language Models
Antoine Gorceix, Bastien Le Chenadec, Ahmad Rammal +2
In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis…
Ordinal Potential-based Player Rating
Nelson Vadori, Rahul Savani
It was recently observed that Elo ratings fail at preserving transitive relations among strategies and therefore cannot correctly extract the transitive component of a game. We pro…
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…
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
Nelson Vadori
One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally,…
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…
A Semi-Markovian Modeling of Limit Order Markets
Anatoliy Swishchuk, Nelson Vadori
R. Cont and A. de Larrard (SIAM J. Finan. Math, 2013) introduced a tractable stochastic model for the dynamics of a limit order book, computing various quantities of interest such…
Law of Large Numbers for Semi-Markov inhomogeneous Random Evolutions on Banach spaces
Nelson Vadori, Anatoliy Swishchuk
Using backward propagators, we construct inhomogeneous Random Evolutions on Banach spaces driven by (uniformly ergodic) Semi-Markov processes. After studying some of their properti…