Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Predictive models are being increasingly used to support consequential decision making at
the individual level in contexts such as pretrial bail and loan approval. As a result, there is …

Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

The growing range of applications of Machine Learning (ML) in a multitude of settings
motivates the ability of computing small explanations for predictions made. Small …

Non-Abelian anyons are exotic quasiparticle excitations hosted by certain topological
phases of matter. They break the fermion-boson dichotomy and obey non-Abelian braiding …

Abstract Explanations of Machine Learning (ML) models often address a question. Such
explanations can be related with selecting feature-value pairs which are sufficient for the …

Symbolic model checking is an important tool for finding bugs (or proving the absence of
bugs) in modern system designs. Because of this, improving the ease of use, scalability, and …

To enable trust in the IC supply chain, logic locking as an IP protection technique received
significant attention in recent years. Over the years, by utilizing Boolean satisfiability (SAT) …

We describe JDart, a dynamic symbolic analysis framework for Java. A distinguishing
feature of JDart is its modular architecture: the main component that performs dynamic …

One of the challenges of deploying machine learning (ML) systems is fairness. Datasets
often include sensitive features, which ML algorithms may unwittingly use to create models …