Adapting Conformal Prediction to Distribution Shifts Without Labels
Adapting Conformal Prediction to Distribution Shifts Without Labels
Conformal prediction (CP) enables machine learning models to output prediction sets with guaranteed coverage rate, assuming exchangeable data. Unfortunately, the exchangeability assumption is frequently violated due to distribution shifts in practice, and the challenge is often compounded by the lack of ground truth labels at test time. Focusing on classification …