6 papers
Adaptive Robust Confidence Sets in High Dimensions
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +1
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We sho…
Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Qiaosen Wang, Chao Gao
Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported la…
On the Impossibility of Specification Testing of Interference Models Based on Exposure Mappings
Chao Gao, Christopher Harshaw, Fredrik Sävje +1
Researchers use interference models based on exposure mappings to facilitate estimation of causal effects in randomized experiments with interference. To test the veracity of such…
Adaptive Confidence Intervals in Efron's Gaussian Two-Groups Model
Qiaosen Wang, Shuwen Chai, Chao Gao
Robust uncertainty quantification is increasingly important in modern data analysis and is often formalized under Huber's model, which allows an -fraction of arbitrary…
Confidence Intervals for Linear Models with Arbitrary Noise Contamination
Dong Xie, Chao Gao, John Lafferty
We study confidence interval construction for linear regression under Huber's contamination model, where an unknown fraction of noise variables is arbitrarily corrupted. While robu…
Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +2
We study the task of noiseless linear regression under Gaussian covariates in the presence of additive oblivious contamination. Specifically, we are given i.i.d.\ samples from a di…