Arbitrage in fractional Brownian motion models. - Patrick Cheridito (Finance Stochastics, 2003)

Coherent and convex monetary risk measures for unbounded càdlàg processes. - Patrick Cheridito; Freddy Delbaen; Michael Kupper (Finance Stochastics, 2005)

Utility maximization under increasing risk aversion in one-period models. - Patrick Cheridito; Christopher Summer (Finance Stochastics, 2006)

Processes of Class Sigma, Last Passage Times, and Drawdowns. - Patrick Cheridito; Ashkan Nikeghbali; Eckhard Platen (SIAM J. Financial Math., 2012)

Pricing and Hedging in Affine Models with Possibility of Default. - Patrick Cheridito; Alexander Wugalter (SIAM J. Financial Math., 2012)

Equilibrium Pricing in Incomplete Markets Under Translation Invariant Preferences. - Patrick Cheridito; Ulrich Horst; Michael Kupper; Traian A. Pirvu (Math. Oper. Res., 2016)

Duality Formulas for Robust Pricing and Hedging in Discrete Time. - Patrick Cheridito; Michael Kupper; Ludovic Tangpi (SIAM J. Financial Math., 2017)

Solving high-dimensional optimal stopping problems using deep learning. - Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Timo Welti (CoRR, 2019)

Efficient approximation of high-dimensional functions with deep neural networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (CoRR, 2019)

Deep Optimal Stopping. - Sebastian Becker; Patrick Cheridito; Arnulf Jentzen (J. Mach. Learn. Res., 2019)

Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (CoRR, 2020)

High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations. - Pierfrancesco Beneventano; Patrick Cheridito; Arnulf Jentzen; Philippe von Wurstemberger (CoRR, 2020)

Landscape analysis for shallow ReLU neural networks: complete classification of critical points for affine target functions. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (CoRR, 2021)

Deep neural network approximation theory for high-dimensional functions. - Pierfrancesco Beneventano; Patrick Cheridito; Robin Graeber; Arnulf Jentzen; Benno Kuckuck (CoRR, 2021)

Non-convergence of stochastic gradient descent in the training of deep neural networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Complex., 2021)

Deep Splitting Method for Parabolic PDEs. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (SIAM J. Sci. Comput., 2021)

A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions. - Patrick Cheridito; Arnulf Jentzen; Adrian Riekert; Florian Rossmannek (J. Complex., 2022)

Landscape Analysis for Shallow Neural Networks: Complete Classification of Critical Points for Affine Target Functions. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Nonlinear Sci., 2022)

Efficient Approximation of High-Dimensional Functions With Neural Networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (IEEE Trans. Neural Networks Learn. Syst., 2022)

Efficient Sobolev approximation of linear parabolic PDEs in high dimensions. - Patrick Cheridito; Florian Rossmannek (CoRR, 2023)

Computation of Conditional Expectations with Guarantees. - Patrick Cheridito; Balint Gersey (J. Sci. Comput., 2023)

An efficient Monte Carlo scheme for Zakai equations. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (Commun. Nonlinear Sci. Numer. Simul., 2023)

Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU Networks. - Patrick Cheridito; Arnulf Jentzen; Florian Rossmannek (J. Optim. Theory Appl., 2024)

Deep Learning for Continuous-time Stochastic Control with Jumps. - Patrick Cheridito; Jean-Loup Dupret; Donatien Hainaut (CoRR, 2025)

Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows. - Gabriele Visentin; Patrick Cheridito (CoRR, 2025)