4 papers · 1 filter
Near-Optimal Cryptographic Hardness of Learning With Homogeneous Halfspaces Under Gaussian Marginals
Jizhou Huang, Brendan Juba
We study three problems that involve identifying homogeneous halfspaces under Gaussian distributions: agnostic learning, one-sided reliable learning, and fairness auditing. In each…
Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distribution
Jizhou Huang, Brendan Juba
In machine learning applications, predictive models are trained to serve future queries across the entire data distribution. Real-world data often demands excessively complex model…
Distribution-Specific Agnostic Conditional Classification With Halfspaces
Jizhou Huang, Brendan Juba
We study ``selective'' or ``conditional'' classification problems under an agnostic setting. Classification tasks commonly focus on modeling the relationship between features and c…
Distribution-Specific Auditing For Subgroup Fairness
Daniel Hsu, Jizhou Huang, Brendan Juba
We study the problem of auditing classifiers with the notion of statistical subgroup fairness. Kearns et al. (2018) has shown that the problem of auditing combinatorial subgroups f…