5 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
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,…
Feasible approximation of matching equilibria for large-scale matching for teams problems
Ariel Neufeld, Qikun Xiang
We propose a numerical algorithm for computing feasible and approximately optimal solutions of the matching for teams problem. Specifically, we introduce the notion of approximate…
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints
Ariel Neufeld, Qikun Xiang
We consider a general class of two-stage distributionally robust optimization (DRO) problems where the ambiguity set is constrained by fixed marginal probability laws that are not…
Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures
Ariel Neufeld, Qikun Xiang
We propose a numerical algorithm for the computation of multi-marginal optimal transport (MMOT) problems involving general probability measures that are not necessarily discrete. B…
A Bonus-Malus Framework for Cyber Risk Insurance and Optimal Cybersecurity Provisioning
Qikun Xiang, Ariel Neufeld, Gareth W. Peters +2
The cyber risk insurance market is at a nascent stage of its development, even as the magnitude of cyber losses is significant and the rate of cyber loss events is increasing. Exis…
Model-free bounds for multi-asset options using option-implied information and their exact computation
Ariel Neufeld, Antonis Papapantoleon, Qikun Xiang
We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their ar…