The goal of this project is to advance the theory and algorithms for machine learning problems that require performance guarantees at subpopulation-level granularity.
- Simple and near-optimal algorithms for hidden stratification and multi-group learning
- Christopher Tosh and Daniel Hsu
- ICML 2022
- Distribution-specific auditing for subgroup fairness
- Daniel Hsu, Jizhou Huang, and Brendan Juba
- FORC 2024
- Multi-group learning for hierarchical groups
- Samuel Deng and Daniel Hsu
- ICML 2024
- Group-wise oracle-efficient algorithms for online multi-group learning
- Samuel Deng, Daniel Hsu, and Jingwen Liu
- NeurIPS 2024
- Group-realizable multi-group learning by minimizing empirical risk
- Navid Ardeshir, Samuel Deng, Daniel Hsu, Jingwen Liu.
- ALT 2026
- Panprediction: optimal predictions for any downstream task and loss
- Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu, Brian Lee, Eric Zhao
- AISTATS 2026
Work on this project has been supported by the ONR under grant N00014-24-1-2700; by the NSF under grants CCF-1740833, IIS-1563785, and IIS-2040971; and by a JP Morgan Faculty Award.