1 citations · 3 across the 7 of their papers we have counts for
7 papers
On efficient robust regression with subquadratic samples
Deeksha Adil, Jarosław Błasiok, Hongjie Chen +1
We revisit the problem of robust linear regression under Gaussian covariates with an unknown covariance matrix of condition number . For this fundamental problem, significant ga…
Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point
Hongjie Chen, Jingqiu Ding, Yiding Hua +1
We study the problem of robustly estimating the edge density of Erdős-Rényi random graphs when an adversary can arbitrarily add or remove edges incident to an …
Outlier-robust Mean Estimation near the Breakdown Point via Sum-of-Squares
Hongjie Chen, Deepak Narayanan Sridharan, David Steurer
We revisit the problem of estimating the mean of a high-dimensional distribution in the presence of an -fraction of adversarial outliers. When is at most…
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
Hongjie Chen, Jingqiu Ding, Yiding Hua +1
We give the first polynomial-time, differentially node-private, and robust algorithm for estimating the edge density of Erdős-Rényi random graphs and their generalization, inhomoge…
Private graphon estimation via sum-of-squares
Hongjie Chen, Jingqiu Ding, Tommaso d'Orsi +3
We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant numbe…
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi +4
We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best know…
On the well-spread property and its relation to linear regression
Hongjie Chen, Tommaso d'Orsi
We consider the robust linear regression model , where an adversary oblivious to the design may choose $\bolds…