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Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks

Dec 16, 2020
Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis

* to appear at AAAI 2021 

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Kernel Interpolation of High Dimensional Scattered Data

Sep 03, 2020
Shao-Bo Lin, Xiangyu Chang, Xingping Sun

* 23 pages, 4 figures 

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Randomized spectral co-clustering for large-scale directed networks

Apr 25, 2020
Xiao Guo, Yixuan Qiu, Hai Zhang, Xiangyu Chang

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Angle-Based Cost-Sensitive Multicategory Classification

Mar 08, 2020
Yi Yang, Yuxuan Guo, Xiangyu Chang

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Randomized Spectral Clustering in Large-Scale Stochastic Block Models

Jan 20, 2020
Hai Zhang, Xiao Guo, Xiangyu Chang

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Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms

Jan 09, 2020
Xiangyu Chang, Shao-Bo Lin

* 32 pages, 2 figures 

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Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

Nov 29, 2017
Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang

* Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) 

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Learning rates for classification with Gaussian kernels

Oct 05, 2017
Shao-Bo Lin, Jinshan Zeng, Xiangyu Chang

* This paper has been accepted by Neural Computation 

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Divide and Conquer Local Average Regression

Mar 13, 2016
Xiangyu Chang, Shaobo Lin, Yao Wang

* 36 pages, 5 figures 

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Sparse K-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional Data Clustering

Mar 31, 2014
Xiangyu Chang, Yu Wang, Rongjian Li, Zongben Xu

* 36 pages, 4 figures, Present the paper at ICSA 2013 

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