15 citations · 31 across the 18 of their papers we have counts for
18 papers
KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks
Siyuan Yang, Cheng Song, Zhilu Lai +1
Differential equations are involved in modeling many engineering problems. Many efforts have been devoted to solving differential equations. Due to the flexibility of neural networ…
Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
Yilun Chen, Wenjia Wang
The Certainty Equivalent heuristic (CE) is a widely-used algorithm for various dynamic resource allocation problems in OR and OM. Despite its popularity, existing theoretical guara…
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…
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions
Wenjia Wang, Yanyuan Wang, Xiaowei Zhang
Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit th…
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu, Jun Wang, Wenjia Wang +1
Deep learning has achieved many breakthroughs in modern classification tasks. Numerous architectures have been proposed for different data structures but when it comes to the loss…
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Wenjia Wang, Bing-Yi Jing
In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regress…
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Tianyang Hu, Wenjia Wang, Cong Lin +1
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data. However, the generalization guarantee may not hold for noisy data. From a…
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…
Reduced Rank Multivariate Kernel Ridge Regression
Wenjia Wang, Yi-Hui Zhou
In the multivariate regression, also referred to as multi-task learning in machine learning, the goal is to recover a vector-valued function based on noisy observations. The vector…
Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors
Wenjia Wang, Yi-Hui Zhou
Classical canonical correlation analysis (CCA) requires matrices to be low dimensional, i.e. the number of features cannot exceed the sample size. Recent developments in CCA have m…
Uncertainty Quantification for Bayesian Optimization
Rui Tuo, Wenjia Wang
Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. A…
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process
Wenjia Wang, Xiaowei Yue, Benjamin Haaland +1
In this paper, we investigate Gaussian process modeling with input location error, where the inputs are corrupted by noise. Here, the best linear unbiased predictor for two cases i…
On the Inference of Applying Gaussian Process Modeling to a Deterministic Function
Wenjia Wang
Gaussian process modeling is a standard tool for building emulators for computer experiments, which are usually used to study deterministic functions, for example, a solution to a…
Kriging prediction with isotropic Matérn correlations: Robustness and experimental design
Rui Tuo, Wenjia Wang
This work investigates the prediction performance of the kriging predictors. We derive some error bounds for the prediction error in terms of non-asymptotic probability under the u…
A Double Penalty Model for Interpretability
Wenjia Wang, Yi-Hui Zhou
Modern statistical learning techniques have often emphasized prediction performance over interpretability, giving rise to "black box" models that may be difficult to understand, an…
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
Wenjia Wang, Rui Tuo, C. F. Jeff Wu
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simp…
Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Chih-Li Sung, Wenjia Wang, Matthew Plumlee +1
The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatl…
Controlling Sources of Inaccuracy in Stochastic Kriging
Wenjia Wang, Benjamin Haaland
Scientists and engineers commonly use simulation models to study real systems for which actual experimentation is costly, difficult, or impossible. Many simulations are stochastic…