A key promise of probabilistic programming is the ability to specify rich models using an
expressive program-ming language. However, the expressive power that makes …

In this paper, we present the first fully-automated expected amortised cost analysis of self-
adjusting data structures, that is, of randomised splay trees, randomised splay heaps and …

We present Tachis, a higher-order separation logic to reason about the expected cost of
probabilistic programs. Inspired by the uses of time credits for reasoning about the running …

Almost-sure termination is an important correctness property for probabilistic programs, and
a number of program logics have been developed for establishing it. However, these logics …

Verification of higher-order probabilistic programs is a challenging problem. We present a
verification method that supports several quantitative properties of higher-order probabilistic …

We propose, implement, and evaluate a hop** proof approach for proving expectation-
based properties of probabilistic programs. Our approach combines EHL, a syntax-directed …

The design of online learning algorithms typically aims to optimise the incurred loss or cost,
eg, the number of classification mistakes made by the algorithm. The goal of this paper is to …

We enhance refinement types with mechanisms to reason about relational properties of
probabilistic computations. Our mechanisms, which are inspired from probabilistic …

In this paper, we study quantitative properties of quantum programs. Properties of interest
include (positive) almost-sure termination, expected runtime or expected cost, that is, for …

A key promise of probabilistic programming is the ability to specify rich models using an
expressive programming language. However, the expressive power that makes probabilistic …