225 citations · 303 across the 17 of their papers we have counts for
4 papers · 1 filter
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Xiangyu Chang, Yingcong Li, Samet Oymak +1
Deep networks are typically trained with many more parameters than the size of the training dataset. Recent empirical evidence indicates that the practice of overparameterization n…
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
Xiangyu Chang, Shao-Bo Lin
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of…
Learning rates for classification with Gaussian kernels
Shao-Bo Lin, Jinshan Zeng, Xiangyu Chang
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some lo…
Divide and Conquer Local Average Regression
Xiangyu Chang, Shaobo Lin, Yao Wang
The divide and conquer strategy, which breaks a massive data set into a se- ries of manageable data blocks, and then combines the independent results of data blocks to obtain a fin…