Abstract: A computer-implemented method, system and computer program product for utilizing a variational autoencoder for neighborhood sampling. A variational autoencoder is trained to generate in-distribution neighborhood samples. Upon training the variational autoencoder to generate in-distribution neighborhood samples, in-distribution neighborhood samples of an instance of a dataset in latent space that satisfy a distortion constraint are generated using the trained variational autoencoder. A set of interpretable examples for the in-distribution neighborhood samples are then generated using a k-nearest neighbors algorithm. Such interpretable examples are then used to explain the black box model's predictions. As a result, the accuracy of the decision making ability of post-hoc local explanation methods is improved.

Abstract: A computer-implemented method, system and computer program product for generating in-distribution samples of data for a neighborhood distribution to be used by post-hoc local explanation methods. An autoencoder is trained to generate in-distribution samples of input data for the neighborhood distribution to be used by a post-hoc local explanation method. Such training includes mapping the input data (e.g., time series data) into a latent dimension (or latent space) forming a first and a second latent code. A mixed code is then obtained by convexly combining the first and second latent codes with a random coefficient. The mixed code is then decoded with the input data masked with interpretable features to obtain conditional mixed reconstructions. Adversarial training is then performed against a discriminator in order to promote in-distribution samples by computing the reconstruction losses of the conditional mixed reconstructions as well as the discriminator losses and then minimizing such losses.