Yijun Bian

Joint Distribution–Informed Shapley Values for Sparse Counterfactual Explanations

Counterfactual explanations (CE) aim to reveal how small input changes flip a model’s prediction, yet many methods modify more features than necessary, reducing clarity and actionability. We introduce COLA, a model- and generator-agnostic post-hoc framework that refines any given CE by computing a c…