Inference after model selection presents computational challenges when
d...
Owing to the promising ability of saving hardware cost and spectrum
reso...
Nowadays, big datasets are spread over many machines which compute in
pa...
Conditional Monte Carlo or pre-integration is a useful tool for reducing...
We propose a method for selective inference after a model selection proc...
Pre-integration is an extension of conditional Monte Carlo to quasi-Mont...
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the...
Many machine learning problems optimize an objective that must be measur...
In network applications, it has become increasingly common to obtain dat...
We participate in the WMT 2020 shared news translation task on Chinese t...
In our "big data" age, the size and complexity of data is steadily
incre...
We provide an exact analysis of the limiting spectrum of matrices random...
The notion of p-value is a fundamental concept in statistical inference ...
We study the following three fundamental problems about ridge regression...
Large datasets create opportunities as well as analytic challenges. A re...
Knowledge base (KB) is an important aspect in artificial intelligence. O...
Effective overload control for large-scale online service system is cruc...