Neural networks are increasingly employed in safety-critical domains. This has prompted
interest in verifying or certifying logically encoded properties of neural networks. Prior work …

Probabilistic inference via model counting has emerged as a scalable technique with strong
formal guarantees, thanks to recent advances in hashing-based approximate counting. State …

The delta debugging problem concerns how to reduce an object while preserving a certain
property, and widely exists in many applications, such as compiler development, regression …

Concolic testing integrates concrete execution (eg, random testing) and symbolic execution
for test case generation. It is shown to be more cost-effective than random testing or …

Essential tasks for the verification of probabilistic programs include bounding expected
outcomes and proving termination in finite expected runtime. We contribute a simple yet …

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the
last twenty years or so, both the Database community and the AI community have studied …

Constrained sampling and counting are two fundamental problems in artificial intelligence
with a diverse range of applications, spanning probabilistic reasoning and planning to …

Modern society is increasingly reliant on the functionality of infrastructure facilities and utility
services. Consequently, there has been surge of interest in the problem of quantification of …

Given a formula ϕ, the problem of uniform sampling seeks to sample solutions of ϕ uniformly
at random. Uniform sampling is a fundamental problem with a wide variety of applications …

Recent years have seen an unprecedented adoption of artificial intelligence in a wide
variety of applications ranging from medical diagnosis, automobile industry, security to …