The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

We present the first systematic study of effectiveness of robustness transformations on a
diverse set of 24 probabilistic programs representing generalized linear models, mixture …

Probabilistic programming languages offer an intuitive way to model uncertainty by
representing complex probability models as simple probabilistic programs. Probabilistic …

Prob-solvable loops are probabilistic programs with polynomial assignments over random
variables and parametrised distributions, for which the full automation of moment-based …

Probabilistic programming is an emerging paradigm enabling software developers to model
uncertainty of real data and to support suitable inference operations directly into computer …

We use algebraic reasoning to translate Bayesian network (BN) properties into linear
recurrence equations over statistical moments of BN variables. We show that this translation …

Probabilistic programming languages aim to describe and automate Bayesian modeling and
inference. Modern languages support programmable inference, which allows users to …

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 …

Probabilistic programs are programs that can draw random samples from probability
distributions and involve random control flows. They are becoming increasingly popular and …