1 code implementation • 19 Feb 2021 • Yang Yu, Shih-Kang Chao, Guang Cheng
We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines.
no code implementations • 24 Sep 2020 • Ben Zhe Wang, Jeffrey Sheen, Stefan Trück, Shih-Kang Chao, Wolfgang Karl Härdle
Monthly disaggregated US data from 1978 to 2016 reveals that exposure to news on inflation and monetary policy helps to explain inflation expectations.
1 code implementation • NeurIPS 2020 • Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng
In the light of the fact that the stochastic gradient descent (SGD) often finds a flat minimum valley in the training loss, we propose a novel directional pruning method which searches for a sparse minimizer in or close to that flat region.
no code implementations • ICML 2020 • Yang Yu, Shih-Kang Chao, Guang Cheng
In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines.
no code implementations • 22 Sep 2019 • Shih-Kang Chao, Guang Cheng
Preliminary empirical analysis of modern image data shows that learning very sparse deep neural networks by gRDA does not necessarily sacrifice testing accuracy.