Adaptive Conformal Inference
Overview
Adaptive conformal inference studies how to update prediction thresholds as outcomes arrive, helping prediction intervals or sets maintain target error rates when data drift or are selected online. Most methods use coverage feedback to adjust a quantile or significance level; variants add sliding windows, recency or density weighting, regime-specific calibration, and score correction. Researchers assess both coverage and efficiency, such as interval width, in settings including time-series forecasting, anomaly detection, and safety-critical control, alongside analyses using online learning and game theory. The central challenge is balancing narrow predictions and responsive adaptation against reliable guarantees: exchangeability supports finite-sample marginal validity for classical conformal methods, while distribution shift and sparse calibration data can cause undercoverage, and stronger conditional guarantees often require tradeoffs or extra assumptions.
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