259 citations · 342 across the 34 of their papers we have counts for
41 papers
Graph Causal Optimal Transport and Wasserstein Distances
Jan Obłój, Vlad Tuchilus
We study the graph causal optimal transport problem, a generalisation of the classical optimal transport problem in which the allowed couplings satisfy causal restrictions prescrib…
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…
The Martingale Sinkhorn Algorithm
Manuel Hasenbichler, Benjamin Joseph, Gregoire Loeper +2
We develop a numerical method for the martingale analogue of the Benamou--Brenier optimal transport problem, which seeks a martingale interpolating two prescribed marginals which i…
Joint Calibration of Local Volatility Models with Stochastic Interest Rates using Semimartingale Optimal Transport
Benjamin Joseph, Gregoire Loeper, Jan Obloj
We develop and implement a non-parametric method for joint exact calibration of a local volatility model and a correlated stochastic short rate model using semimartingale optimal t…
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…