1 citations · 2 across the 5 of their papers we have counts for
5 papers
Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions
Chengmei Niu, Sachin Garg, Michał Dereziński +1
Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses,…
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
Chengmei Niu, Zhenyu Liao, Zenan Ling +1
A substantial body of work in machine learning (ML) and randomized numerical linear algebra (RandNLA) has exploited various sorts of random sketching methodologies, including rando…
Analysis and Approximate Inference of Large Random Kronecker Graphs
Zhenyu Liao, Yuanqian Xia, Chengmei Niu +1
Random graph models are playing an increasingly important role in various fields ranging from social networks, telecommunication systems, to physiologic and biological networks. Wi…
Optimal subsampling for functional quantile regression
Qian Yan, Hanyu Li, Chengmei Niu
Subsampling is an efficient method to deal with massive data. In this paper, we investigate the optimal subsampling for linear quantile regression when the covariates are functions…
Optimal Sampling Algorithms for Block Matrix Multiplication
Chengmei Niu, Hanyu Li
In this paper, we investigate the randomized algorithms for block matrix multiplication from random sampling perspective. Based on the A-optimal design criterion, the optimal sampl…