15 citations · 69 across the 9 of their papers we have counts for
10 papers
Balancing Approach for Causal Inference at Scale
Sicheng Lin, Meng Xu, Xi Zhang +3
With the modern software and online platforms to collect massive amount of data, there is an increasing demand of applying causal inference methods at large scale when randomized e…
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
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. The method produces an $\ell…
A note on the impact of news on US household inflation expectations
Ben Zhe Wang, Jeffrey Sheen, Stefan Trück +2
Monthly disaggregated US data from 1978 to 2016 reveals that exposure to news on inflation and monetary policy helps to explain inflation expectations. This remains true when contr…
Directional Pruning of Deep Neural Networks
Shih-Kang Chao, Zhanyu Wang, Yue Xing +1
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 sear…
Simultaneous Inference for Massive Data: Distributed Bootstrap
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. This new method is computationally efficient in that we…
A generalization of regularized dual averaging and its dynamics
Shih-Kang Chao, Guang Cheng
Excessive computational cost for learning large data and streaming data can be alleviated by using stochastic algorithms, such as stochastic gradient descent and its variants. Rece…
Distributed inference for quantile regression processes
Stanislav Volgushev, Shih-Kang Chao, Guang Cheng
The increased availability of massive data sets provides a unique opportunity to discover subtle patterns in their distributions, but also imposes overwhelming computational challe…
Quantile Processes for Semi and Nonparametric Regression
Shih-Kang Chao, Stanislav Volgushev, Guang Cheng
A collection of quantile curves provides a complete picture of conditional distributions. Properly centered and scaled versions of estimated curves at various quantile levels give…
Factorisable Multitask Quantile Regression
Shih-Kang Chao, Wolfgang Karl Härdle, Ming Yuan
A multivariate quantile regression model with a factor structure is proposed to study data with many responses of interest. The factor structure is allowed to vary with the quantil…
Confidence Corridors for Multivariate Generalized Quantile Regression
Shih-Kang Chao, Katharina Proksch, Holger Dette +1
We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for…