Many Machine Learning (ML) algorithms are in-herently random in nature-executing them
using the same inputs may lead to slightly different results across different runs. Such …

Probabilistic programming aims to open the power of Bayesian reasoning to software
developers and scientists, but identification of problems during inference and debugging are …

We propose a novel tool, AquaSense, to automatically reason about the sensitivity analysis
of probabilistic programs. In the context of probabilistic programs, sensitivity analysis …

Probabilistic programming simplifies the encoding of statistical models as straightforward
programs. At its core, it employs an inference algorithm which automate the model inference …

Abstract Machine Learning (ML) is rapidly revolutionizing the way modern-day systems are
developed. However, testing ML-based systems is challenging due to 1) the presence of …

The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture
Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has …