9 papers
Robust -learning for mean-field control under Wasserstein uncertainty in common noise
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
In this article, we present a robust -learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law. The algorithm combi…
Scaling limits of multi-period distributionally robust optimization problems
Max Nendel, Ariel Neufeld, Kyunghyun Park +1
We examine the scaling limit of multi-period distributionally robust optimization (DRO) problems via a semigroup approach. Each period involves a worst-case maximization over distr…
Quantum Monte Carlo algorithm for option pricing and its complexity analysis
Jianjun Chen, Yongming Li, Ariel Neufeld
In this paper we provide a quantum Monte Carlo algorithm to solve multidimensional Black-Scholes PDEs with correlation for option pricing. The payoff function of the option is of g…
Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures
Zeyi Chen, Ariel Neufeld, Qikun Xiang
We develop an estimator-based stochastic fixed-point framework for approximately computing the 2-Wasserstein barycenter of continuous, non-parametric probability measures. Notably,…
Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis
Daniel Bartl, Ariel Neufeld, Kyunghyun Park
We examine nonlinear Kolmogorov partial differential equations (PDEs). Here the nonlinear part of the PDE comes from its Hamiltonian where one maximizes over all possible drift and…
Robust mean-field control under common noise uncertainty
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
We propose and analyze a framework for discrete-time robust mean-field control problems under common noise uncertainty. In this framework, the mean-field interaction describes the…