Robustness is widely regarded as a fundamental problem in the analysis of machine learning (ML) models. Most often robustness equates with deciding the non-existence of …
Neural networks are being applied to a wide range of tasks in autonomous systems, such as perception, prediction, planning, control, and general decision making. While they may …
Models of actual causality leverage domain knowledge to generate convincing diagnoses of events that caused an outcome. It is promising to apply these models to diagnose and repair …
Deep neural networks have demonstrated impressive performance in a wide variety of applications. However, deep neural networks are not perfect. In many cases, additional …
Many works have been devoted to evaluating the robustness of a classifier in the neighborhood of single points of input data. Recently, in particular, probabilistic settings …