34 citations · 49 across the 9 of their papers we have counts for
9 papers
Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
Yihang Gao, Michael K. Ng, Michael W. Mahoney +1
We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and…
NLAFormer: Transformers Learn Numerical Linear Algebra Operations
Zhantao Ma, Yihang Gao, Michael K. Ng
Transformers are effective and efficient at modeling complex relationships and learning patterns from structured data in many applications. The main aim of this paper is to propose…
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
Yihang Gao, Chuanyang Zheng, Enze Xie +6
Besides natural language processing, transformers exhibit extraordinary performance in solving broader applications, including scientific computing and computer vision. Previous wo…
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
Yihang Gao, Ka Chun Cheung, Michael K. Ng
Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of…
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization
Yihang Gao, Michael K. Ng
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global con…
Approximate Secular Equations for the Cubic Regularization Subproblem
Yihang Gao, Man-Chung Yue, Michael K. Ng
The cubic regularization method (CR) is a popular algorithm for unconstrained non-convex optimization. At each iteration, CR solves a cubically regularized quadratic problem, calle…
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Yihang Gao, Huafeng Liu, Michael K. Ng +1
Wide applications of differentiable two-player sequential games (e.g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast…
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yihang Gao, Michael K. Ng
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equ…
Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks
Yihang Gao, Michael K. Ng, Mingjie Zhou
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation f…