30 citations · 69 across the 10 of their papers we have counts for
6 papers · 1 filter
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
Yu Cheng, Honghao Lin
We study the problem of learning Bayesian networks where an -fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying grap…
Fair for All: Best-effort Fairness Guarantees for Classification
Anilesh K. Krishnaswamy, Zhihao Jiang, Kangning Wang +2
Standard approaches to group-based notions of fairness, such as \emph{parity} and \emph{equalized odds}, try to equalize absolute measures of performance across known groups (based…
High-Dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng, Ilias Diakonikolas, Rong Ge +1
We study the problem of high-dimensional robust mean estimation in the presence of a constant fraction of adversarial outliers. A recent line of work has provided sophisticated pol…
Faster Algorithms for High-Dimensional Robust Covariance Estimation
Yu Cheng, Ilias Diakonikolas, Rong Ge +1
We study the problem of estimating the covariance matrix of a high-dimensional distribution when a small constant fraction of the samples can be arbitrarily corrupted. Recent work…
High-Dimensional Robust Mean Estimation in Nearly-Linear Time
Yu Cheng, Ilias Diakonikolas, Rong Ge
We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted. Recent work gave the fi…
Non-Convex Matrix Completion Against a Semi-Random Adversary
Yu Cheng, Rong Ge
Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the cu…