19 citations · 22 across the 12 of their papers we have counts for
12 papers
Renewable high-dimensional expected shortfall regression
Haochen Rao, Tingzi Weng, Yifan Jiang +1
Expected Shortfall (ES) has become a core coherent risk measure in finance and statistics, and high-dimensional ES regression is crucial for characterizing heterogeneous tail risk…
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…
Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
Yasong Feng, Yifan Jiang, Tianyu Wang +1
This work provides a novel convergence analysis for stochastic optimization in terms of stopping times, addressing the practical reality that algorithms are often terminated adapti…
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…
Wasserstein distributional adversarial training for deep neural networks
Xingjian Bai, Guangyi He, Yifan Jiang +1
Design of adversarial attacks for deep neural networks, as well as methods of adversarial training against them, are subject of intense research. In this paper, we propose methods…
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…
Wasserstein distributional robustness of neural networks
Xingjian Bai, Guangyi He, Yifan Jiang +1
Deep neural networks are known to be vulnerable to adversarial attacks (AA). For an image recognition task, this means that a small perturbation of the original can result in the i…
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…
Existence and Distributional Chaos of Points that are Recurrent but Not Banach Recurrent
Yifan Jiang, Xueting Tian
According to the recurrent frequency, many levels of recurrent points are found, such as periodic points, almost periodic points, weakly almost periodic points, quasi-weakly almost…