3 papers
cs.LG2026
A One-Inclusion Graph Approach to Multi-Group Learning
Noah Bergam, Samuel Deng, Daniel Hsu
We prove the tightest-known upper bounds on the sample complexity of multi-group learning. Our algorithm extends the one-inclusion graph prediction strategy using a generalization…
cs.LG2026
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
Lujing Zhang, Daniel Hsu, Sivaraman Balakrishnan
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools…
cs.LG2024
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…