  Using feature attributions for post-hoc explanations is a common practice to
understand and verify the predictions of opaque machine learning models.
Despite the numerous techniques available, individual methods often produce
inconsistent and unstable results, putting their overall reliability into
question. In this work, we aim to systematically improve the quality of feature
attributions by combining multiple explanations across distinct methods or
their variations. For this purpose, we propose a novel approach to derive
optimal convex combinations of feature attributions that yield provable
improvements of desired quality criteria such as robustness or faithfulness to
the model behavior. Through extensive experiments involving various model
architectures and popular feature attribution techniques, we demonstrate that
our combination strategy consistently outperforms individual methods and
existing baselines.
