3 papers
stat.ML2019
Inference and Uncertainty Quantification for Noisy Matrix Completion
Yuxin Chen, Jianqing Fan, Cong Ma +1
Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it…
stat.ML2019
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
Yuxin Chen, Yuejie Chi, Jianqing Fan +2
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and effici…
math.ST2018
Asymptotic Seed Bias in Respondent-driven Sampling
Yuling Yan, Bret Hanlon, Sebastien Roch +1
Respondent-driven sampling (RDS) collects a sample of individuals in a networked population by incentivizing the sampled individuals to refer their contacts into the sample. This i…