Distribution Shift Robustness

The field asks how models can remain accurate, calibrated, and fair when data change across time, locations, populations, or conditions, including shifts in inputs, labels, and their relationships. Approaches include distributionally robust optimization, invariant and causal representations, targeted data augmentation, test-time adaptation, and conformal methods that adjust predictions or quantify uncertainty. Evaluation increasingly uses temporal and cross-domain tests, realistic corruption benchmarks, and worst-group measures rather than relying only on random train-test splits. A central tension is that adapting to a new domain can improve its performance but also cause instability or forget earlier capabilities, while guarantees that cover broad shifts may produce overly conservative predictions.