Overview
Stephen Bates is a statistician (MIT; previously UC Berkeley), working on distribution-free inference, conformal prediction, and reliable machine learning.
Key Contributions / Features
- Gentle introduction to conformal prediction (Angelopoulos-Bates 2023): with Angelopoulos, the standard tutorial on conformal prediction and distribution-free uncertainty quantification.
- Work on conformal risk control, distribution-free calibration, and testing/selective-inference guarantees for predictive models.
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