Many datasets are of increasingly high dimension-ality, where a large number of features
could be irrelevant to the learning task. The inclusion of such features would not only …

We present Learning to Explain (LTX), a model-agnostic framework designed for providing
post-hoc explanations for vision models. The LTX framework introduces an “explainer” …

The quest for transparency in black-box models has gained significant momentum in recent
years. In particular, discovering the underlying machine learning technique type (or model …

Deep Learning models provide state of the art classification results, but are not human-
interpretable. We propose a novel method to interpret the classification results of a black box …

Interpretable surrogates of black-box predictors trained on high-dimensional tabular
datasets can struggle to generate comprehensible explanations in the presence of …

The research around developing methods for debugging and refining Machine Learning
(ML) models is still in its infancy. We believe employing tailored tools in the development …

Defining a representative locality is an urgent challenge in perturbation-based explanation
methods, which influences the fidelity and soundness of explanations. We address this issue …

As machine learning algorithms are deployed ubiquitously to a variety of domains, it is
imperative to make these often black-box models transparent. Several recent works explain …

Complex black-box predictive models may have high accuracy, but opacity causes problems
like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable …

In recent years, understanding the decision-making process of black-box models has
become not only a legal requirement but also an additional way to assess their performance …