CONDITION COVERAGE IN SPARSE TARGET CALIBRATION FOR DOMAIN ADAPTATION

We study domain adaptation when target labels are available only at a sparse set of calibration levels, while prediction is required across the full target label space. We show that the observed target distributions do not uniquely determine the target distributions at unseen levels, even when the observed distributions are known exactly. This motivates a distributionally robust formulation that accounts for uncertainty induced by incomplete calibration coverage. Based on the resulting robust bound, we propose CaDRA, a Calibration-aware Distributionally Robust Adaptation method that combines labeled target prediction with balanced source– target alignment. Controlled synthetic experiments and three real-world transfer tasks show consistent performance across different calibration patterns and domain shifts.