Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded
global healthcare system, which receives approximately 60 million primary care visits and 6 …

Drug discovery aims to find novel compounds with specified chemical property profiles. In
terms of generative modeling, the goal is to learn to sample molecules in the intersection of …

With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …

The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep
learning. Deep learning methods are remarkably accurate, but also opaque, which limits …

Selection of input features such as relevant pieces of text has become a common technique
of highlighting how complex neural predictors operate. The selection can be optimized post …

Most of the work on interpretable machine learning has focused on designing either
inherently interpretable models, which typically trade-off accuracy for interpretability, or post …

To tackle interpretability in deep learning, we present a novel framework to jointly learn a
predictive model and its associated interpretation model. The interpreter provides both local …

The ability to identify influential training examples enables us to debug training data and
explain model behavior. Existing techniques to do so are based on the flow of training data …

Concept-based interpretations of black-box models are often more intuitive for humans to
understand. The most widely adopted approach for concept-based interpretation is Concept …

The increasing success of deep neural networks has raised concerns about their inherent
black-box nature, posing challenges related to interpretability and trust. While there has …