8 papers
Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms
Bingye Ni, Xiaoyu Wang, Yingli Wang +1
We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to t…
Accelerating Constrained Sampling: A Large Deviations Approach
Yingli Wang, Changwei Tu, Xiaoyu Wang +1
The problem of sampling a target probability distribution on a constrained domain arises in many applications including machine learning. For constrained sampling, various Langevin…
VIX and European options with jumps in the short-maturity regime
Desen Guo, Dan Pirjol, Xiaoyu Wang +1
We present a study of the short-maturity asymptotics for VIX and European option prices in local-stochastic volatility models with compound Poisson jumps. Both out-of-the-money (OT…
Sampling non-log-concave densities via Hessian-free high-resolution dynamics
Xiaoyu Wang, Yingli Wang, Lingjiong Zhu
We study the problem of sampling from a target distribution on , where can be non-convex, via the Hessian-free high-resolution (HFHR) dyn…
Generalized EXTRA stochastic gradient Langevin dynamics
Mert Gurbuzbalaban, Mohammad Rafiqul Islam, Xiaoyu Wang +1
Langevin algorithms are popular Markov Chain Monte Carlo methods for Bayesian learning, particularly when the aim is to sample from the posterior distribution of a parametric model…
Short-maturity options on realized variance in local-stochastic volatility models
Dan Pirjol, Xiaoyu Wang, Lingjiong Zhu
We derive the short-maturity asymptotics for prices of options on realized variance in local-stochastic volatility models. We consider separately the short-maturity asymptotics for…