12 papers
Multi-Level Market Making with Reinforcement Learning
Patrick Cheridito, Moritz Weiss
We introduce a reinforcement learning framework for market making in a limit order book. Our algorithm aims to maximize trading revenue by dynamically submitting market and limit o…
Physics-informed diffusion models in spectral space
Davide Gallon, Philippe von Wurstemberger, Patrick Cheridito +1
We propose physics-informed spectral diffusion (PISD), a methodology that combines generative latent diffusion models with physics-informed machine learning to generate solutions o…
INEUS: Iterative Neural Solver for High-Dimensional PIDEs
Jean-Loup Dupret, Davide Gallon, Patrick Cheridito
In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal ju…
Deep neural network approximation theory for high-dimensional functions
Pierfrancesco Beneventano, Patrick Cheridito, Robin Graeber +2
The purpose of this article is to develop a machinery to study the capacity of deep neural networks (DNNs) to approximate high-dimensional functions. In particular, we show that DN…
Reinforcement Learning for Trade Execution with Market and Limit Orders
Patrick Cheridito, Moritz Weiss
In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic alloca…
Deep Legendre Transform
Aleksey Minabutdinov, Patrick Cheridito
We introduce a novel deep learning algorithm for computing convex conjugates of differentiable convex functions, a fundamental operation in convex analysis with various application…