8 citations · 26 across the 13 of their papers we have counts for
13 papers
Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding, Tianyang Hu, Jiahang Jiang +3
Random smoothing data augmentation is a unique form of regularization that can prevent overfitting by introducing noise to the input data, encouraging the model to learn more gener…
Representing Additive Gaussian Processes by Sparse Matrices
Lu Zou, Haoyuan Chen, Liang Ding
Among generalized additive models, additive Matérn Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure a…
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Haoyuan Chen, Liang Ding, Rui Tuo
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Matérn correlations whose smoothness parameter is a half-integer. The proposed a…
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…
Sample and Computationally Efficient Stochastic Kriging in High Dimensions
Liang Ding, Xiaowei Zhang
Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where 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…
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Liang Ding, Lu Zou, Wenjia Wang +2
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization. The main bottleneck in high-dimensional density estimation is the p…
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…
Knowledge Gradient for Selection with Covariates: Consistency and Computation
Liang Ding, L. Jeff Hong, Haihui Shen +1
Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper we consider the ranking and selection pr…
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…
New Quantitative Deformation Lemma and New Mountain Pass Theorem
Liang Ding, Fode Zhang, Shiqing Zhang
In this paper, we obtain a new quantitative deformation Lemma so that we can obtain more critical points, especially for supinf critical value , is a new criti…