135 citations · 666 across the 42 of their papers we have counts for
46 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…
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…
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…
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…
ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Optimal Execution with Endogenous Liquidity
Patrick Cheridito, Jean-Loup Dupret, Zhexin Wu
Classical optimal execution models treat market impact as a pre-specified, exogenous process. However, when market makers adapt strategically, this assumption becomes a structural…
Robust Optimization in Causal Models and G-Causal Normalizing Flows
Gabriele Visentin, Patrick Cheridito
In this paper, we show that interventionally robust optimization problems in causal models are continuous under the -causal Wasserstein distance, but may be discontinuous under…