A method (300) for training a locally interpretable model (190) includes obtaining a set of training samples (130), and training a black-box model (120) using the set of training samples. The method also includes generating, using the trained black-box model and the set of training samples, a set of auxiliary training samples (140) and training a baseline interpretable model (150) using the set of auxiliary training samples. The method also includes training, using the set of auxiliary training samples and baseline interpretable model an instance-wise weight estimator model (160). For each auxiliary training sample, the method also includes determining, using the trained instance-wise weight estimator model, a selection probability (170) for the auxiliary training sample. The method also includes selecting, based on the selection probabilities, a subset of auxiliary training samples (140S) and training the locally interpretable model using the subset of auxiliary training samples.
REINFORCEMENT LEARNING BASED LOCALLY INTERPRETABLE MODELS