no code implementations • 22 Oct 2024 • Antoine Gorceix, Bastien Le Chenadec, Ahmad Rammal, Nelson Vadori, Manuela Veloso
In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations.
no code implementations • 8 Jun 2023 • 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.
no code implementations • 13 Oct 2022 • Nelson Vadori, Leo Ardon, Sumitra Ganesh, Thomas Spooner, Selim Amrouni, Jared Vann, Mengda Xu, Zeyu Zheng, Tucker Balch, Manuela Veloso
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.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
no code implementations • 14 Mar 2022 • 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.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 13 Oct 2021 • Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh
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.
no code implementations • 4 Jun 2021 • Nelson Vadori, Rahul Savani, Thomas Spooner, Sumitra Ganesh
Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whether the game is zero-sum or cooperative.
no code implementations • NeurIPS 2021 • Thomas Spooner, Nelson Vadori, Sumitra Ganesh
In this paper, we address this problem through a factor baseline which exploits independence structure encoded in a novel action-target influence network.
no code implementations • NeurIPS 2020 • Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso
Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems.
no code implementations • 23 Jun 2020 • Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso
We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems.
1 code implementation • 14 Nov 2019 • Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng, Prashant Reddy, Manuela Veloso
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.