Patrick Cheridito

According to our database, Patrick Cheridito authored at least 23 papers between 2003 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

An efficient Monte Carlo scheme for Zakai equations.

Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld

Commun. Nonlinear Sci. Numer. Simul., November, 2023

Computation of Conditional Expectations with Guarantees.

Patrick Cheridito, Balint Gersey

J. Sci. Comput., April, 2023

Efficient Sobolev approximation of linear parabolic PDEs in high dimensions.

Patrick Cheridito, Florian Rossmannek

CoRR, 2023

2022

Efficient Approximation of High-Dimensional Functions With Neural Networks.

Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek

IEEE Trans. Neural Networks Learn. Syst., 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

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

Gradient descent provably escapes saddle points in the training of shallow ReLU networks.

Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek

CoRR, 2022

2021

Deep Splitting Method for Parabolic PDEs.

Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld

SIAM J. Sci. Comput., 2021

Non-convergence of stochastic gradient descent in the training of deep neural networks.

Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek

J. Complex., 2021

Deep neural network approximation theory for high-dimensional functions.

Pierfrancesco Beneventano, Patrick Cheridito, Robin Graeber, Arnulf Jentzen, Benno Kuckuck

CoRR, 2021

Landscape analysis for shallow ReLU neural networks: complete classification of critical points for affine target functions.

Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek

CoRR, 2021

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

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

2019

Deep Optimal Stopping.

Sebastian Becker, Patrick Cheridito, Arnulf Jentzen

J. Mach. Learn. Res., 2019

Efficient approximation of high-dimensional functions with deep neural networks.

Patrick Cheridito, Arnulf Jentzen, Florian Rossmannek

CoRR, 2019

Solving high-dimensional optimal stopping problems using deep learning.

Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Timo Welti

CoRR, 2019

2017

Duality Formulas for Robust Pricing and Hedging in Discrete Time.

Patrick Cheridito, Michael Kupper, Ludovic Tangpi

SIAM J. Financial Math., 2017

2016

Equilibrium Pricing in Incomplete Markets Under Translation Invariant Preferences.

Patrick Cheridito, Ulrich Horst, Michael Kupper, Traian A. Pirvu

Math. Oper. Res., 2016

2012

Pricing and Hedging in Affine Models with Possibility of Default.

Patrick Cheridito, Alexander Wugalter

SIAM J. Financial Math., 2012

Processes of Class Sigma, Last Passage Times, and Drawdowns.

Patrick Cheridito, Ashkan Nikeghbali, Eckhard Platen

SIAM J. Financial Math., 2012

2006

Utility maximization under increasing risk aversion in one-period models.

Patrick Cheridito, Christopher Summer

Finance Stochastics, 2006

2005

Coherent and convex monetary risk measures for unbounded càdlàg processes.

Patrick Cheridito, Freddy Delbaen, Michael Kupper

Finance Stochastics, 2005

2003

Arbitrage in fractional Brownian motion models.

Patrick Cheridito

Finance Stochastics, 2003