Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

The growing popularity and adoption of differential privacy in academic and industrial
settings has resulted in the development of increasingly sophisticated algorithms for …

The adoption of differential privacy is growing but the complexity of designing private,
efficient and accurate algorithms is still high. We propose a novel programming framework …

During the past decade, differential privacy has become the gold standard for protecting the
privacy of individuals. However, verifying that a particular program provides differential …

Differential privacy offers a formal framework for reasoning about privacy and accuracy of
computations on private data. It also offers a rich set of building blocks for constructing …

Differential privacy is a promising approach to formalizing privacy—that is, for writing down
what privacy means as a mathematical equation. This book is provides overview of …

Probabilistic programming provides a convenient lingua franca for writing succinct and
rigorous descriptions of probabilistic models and inference tasks. Several probabilistic …

The Fuzz programming language by Reed and Pierce uses an elegant linear type system
combined with a monad-like type to express and reason about probabilistic sensitivity …

This study considers the Bayesian programming methodology for recognition and
classification of radio emission sources. A mathematical model of Bayesian programming …

Abstract Machine Learning (ML) Classification has already become one of the most
commonly used techniques in many areas such as banking, medicine, spam detection and …