2 citations · 2 across the 3 of their papers we have counts for
6 papers
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
Liang Ding, Rui Tuo, Xiaowei Zhang
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the…
A projected gradient method for sparsity regularization
Liang Ding, Weimin Han
The non-convex regularization has attracted attention in the field of sparse recovery. One way to obtain a minimizer of th…
sparsity regularization for nonlinear ill-posed problems
Liang Ding, Weimin Han
In this paper, we consider the sparsity regularization with parameter for nonlinear ill-posed inverse problems. We investi…
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Liang Ding, Rui Tuo, Shahin Shahrampour
Despite their success, kernel methods suffer from a massive computational cost in practice. In this paper, in lieu of commonly used kernel expansion with respect to inputs, we…
BdryGP: a new Gaussian process model for incorporating boundary information
Liang Ding, Simon Mak, C. F. Jeff Wu
Gaussian processes (GPs) are widely used as surrogate models for emulating computer code, which simulate complex physical phenomena. In many problems, additional boundary informati…
Scalable Stochastic Kriging with Markovian Covariances
Liang Ding, Xiaowei Zhang
Stochastic kriging is a popular technique for simulation metamodeling due to its exibility and analytical tractability. Its computational bottleneck is the inversion of a covarianc…