This paper is devoted to studying the semi-supervised sparse statistical...
Multi-task learning has attracted much attention due to growing multi-pu...
Nonresponse frequently arises in practice, and simply ignoring it may le...
Privacy-preserving data analysis has become prevailing in recent years. ...
Non-probability sampling is prevailing in survey sampling, but ignoring ...
Decentralized sparsity learning has attracted a significant amount of
at...
Random forests are a widely used machine learning algorithm, but their
c...
Matrix completion is a prevailing collaborative filtering method for
rec...
Tensor completion aims at filling the missing or unobserved entries base...
Recently, textual information has been proved to play a positive role in...
In this paper, we propose a novel method for matrix completion under gen...
This paper develops an efficient distributed inference algorithm, which ...
We consider the problem of discrete distribution estimation under locall...
In this paper, we consider matrix completion with absolute deviation los...
This paper investigates the problem of adjusting for spatial effects in
...
Survey data are the gold-standard for estimating finite population
param...
This paper studies distributed estimation and support recovery for
high-...
This paper investigates the problem of matrix completion from corrupted ...