Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

The recent release of large-scale healthcare datasets has greatly propelled the research of
data-driven deep learning models for healthcare applications. However, due to the nature of …

Tree-based machine learning models such as random forests, decision trees and gradient
boosted trees are popular nonlinear predictive models, yet comparatively little attention has …

Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Membership inference (MI) attacks exploit the fact that machine learning algorithms
sometimes leak information about their training data through the learned model. In this work …

There is general consensus that it is important for artificial intelligence (AI) and machine
learning systems to be explainable and/or interpretable. However, there is no general …

Deep neural networks, including recurrent networks, have been successfully applied to
human activity recognition. Unfortunately, the final representation learned by recurrent …

Abstract We develop Neuron Shapley as a new framework to quantify the contribution of
individual neurons to the prediction and performance of a deep network. By accounting for …