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Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
The concept class of low-degree polynomial threshold functions (PTFs) plays a fundamental role in machine learning.
List-Decodable Sparse Mean Estimation
Robust mean estimation is one of the most important problems in statistics: given a set of samples in $\mathbb{R}^d$ where an $\alpha$ fraction are drawn from some distribution $D$ and the rest are adversarially corrupted, we aim to estimate the mean of $D$.
Semi-verified PAC Learning from the Crowd
We study the problem of crowdsourced PAC learning of threshold functions.
Efficient PAC Learning from the Crowd with Pairwise Comparisons
We study crowdsourced PAC learning of threshold functions, where the labels are gathered from a pool of annotators some of whom may behave adversarially.
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