19 citations · 22 across the 12 of their papers we have counts for
7 papers · 1 filter
Schrödinger bridge with transport relaxation
Yifan Jiang, Renyuan Xu, Luhao Zhang
Motivated by modern machine learning applications where we only have access to empirical measures constructed from finite samples, we relax the marginal constraints of the classica…
A transfer principle for computing the adapted Wasserstein distance between stochastic processes
Yifan Jiang, Fang Rui Lim
We propose a transfer principle to study the adapted 2-Wasserstein distance between stochastic processes. First, we obtain an explicit formula for the distance between real-valued…
Sensitivity of causal distributionally robust optimization
Yifan Jiang, Jan Obloj
We study the causal distributionally robust optimization (DRO) in both discrete- and continuous- time settings. The framework captures model uncertainty, with potential models pena…
Duality of causal distributionally robust optimization
Yifan Jiang
We study distributionally robust optimization (DRO) in a dynamic context, where model uncertainty is captured by penalizing potential models based on their adapted Wasserstein dist…
Sequential propagation of chaos
Kai Du, Yifan Jiang, Xiaochen Li
A new class of particle systems with sequential interaction is proposed to approximate the McKean-Vlasov process that originally arises as the limit of the mean-field interacting p…
Empirical approximation to invariant measures for McKean--Vlasov processes: mean-field interaction vs self-interaction
Kai Du, Yifan Jiang, Jinfeng Li
This paper proves that, under a monotonicity condition, the invariant probability measure of a McKean--Vlasov process can be approximated by weighted empirical measures of some pro…
Convergence of the Deep BSDE method for FBSDEs with non-Lipschitz coefficients
Yifan Jiang, Jinfeng Li
This paper is dedicated to solving high-dimensional coupled FBSDEs with non-Lipschitz diffusion coefficients numerically. Under mild conditions, we provided a posterior estimate of…