1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ME2026
Dependable Exploitation of High-Dimensional Unlabeled Data in an Assumption-Lean Framework
Chao Ying, Siyi Deng, Yang Ning +2
Semi-supervised learning has attracted significant attention due to the proliferation of applications featuring limited labeled data but abundant unlabeled data. In this paper, we…
stat.ML2021★ 1 cited
Optimal Variable Clustering for High-Dimensional Matrix Valued Data
Inbeom Lee, Siyi Deng, Yang Ning
Matrix valued data has become increasingly prevalent in many applications. Most of the existing clustering methods for this type of data are tailored to the mean model and do not a…
stat.ME2020
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Siyi Deng, Yang Ning, Jiwei Zhao +1
We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimati…