We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of predicates. show how existing inference and techniques the underlying ProbLog can be adapted for new language. theoretically experimentally demonstrate DeepProbLog supports (i) both symbolic subsymbolic representations inference, (ii) program induction, (iii) (logic) programmi...

This paper briefly describes three well-established frameworks for handling uncertainty in optimization problems. Our focus is mainly on combinatorial optimization and on the development of approximation algorithms under the discussed frameworks. In particular, we give a brief overview of Stochastic Programming, Robust Optimization, and Probabilistic Combinatorial Optimization, and list approxi...

We make a formal analogy between random sampling and fresh name generation. show that quasi-Borel spaces, model for probabilistic programming, can soundly interpret the ν-calculus, calculus Moreover, we prove this semantics is fully abstract up to first-order types. This surprising an ‘off-the-shelf’ model, requires novel analysis of probability distributions on function spaces. Our tools are d...