Overview
Anastasios N. Angelopoulos is a machine-learning researcher (UC Berkeley), known for distribution-free uncertainty quantification and conformal prediction.
Key Contributions / Features
- Gentle introduction to conformal prediction (Angelopoulos-Bates 2023): the widely used tutorial on split conformal prediction and distribution-free UQ.
- Work on conformal risk control, RAPS/adaptive prediction sets, and distribution-free calibration for structured prediction.
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