10 papers
Accelerating Non-Smooth and Heavy-Tailed Sampling
Pervez Ali, Xiaoyu Wang, Yingli Wang +1
Anchored Langevin dynamics (ALD) is useful for non-smooth sampling where the density of the target distribution is possibly non-differentiable and heavy-tailed; reflected anchored…
Improved Analysis for Hessian-free High-resolution Monte Carlo Sampling
Wujun Lv, Xiaoyu Wang, Yingli Wang +1
Hessian-free high-resolution (HFHR) dynamics augments underdamped Langevin dynamics (ULD) with reversible position diffusion for sampling problems that arise in machine learning. W…
Microstructural Foundation for the Rough Hawkes--Heston Model
Yingli Wang, Yinhao Wu, Lingjiong Zhu
Hawkes-based microstructural foundations for rough volatility, leverage, and rough Heston-type limits were developed by El Euch et al. (2018, Finance Stoch., 22(2), 241--280) and c…
An Eyring--Kramers Law for the Hypoelliptic Third-Order Langevin Diffusion
Yingli Wang, Lingjiong Zhu
We prove an Eyring--Kramers law for metastable transitions of the hypoelliptic third-order Langevin diffusion in the low-temperature limit. This diffusion is a three-level Markovia…
Weak Equilibrium Measures and Capacity--Hitting Identities for the Hypoelliptic Third-Order Langevin Diffusion
Ping He, Xiaodan Li, Yingli Wang +1
We construct weak equilibrium measures and weak capacities for the hypoelliptic third-order Langevin diffusion motivated by an accelerated sampling algorithm (Mou et al. (2021) \te…
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…
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) dyna…
Regime-Switching Langevin Monte Carlo Algorithms
Xiaoyu Wang, Yingli Wang, Lingjiong Zhu
Langevin Monte Carlo (LMC) algorithms are popular Markov Chain Monte Carlo (MCMC) methods to sample a target probability distribution, which arises in many applications in machine…
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…
Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR processes: Weak-error bounds and option pricing
Yingli Wang, Zhenyu Cui, Lingjiong Zhu
We study nearly unstable bivariate cumulative heavy-tailed INAR() processes and show that, under a one-factor parameterization and a suitable scaling, they converge to the…