Currently, there is a gap between the tools used by probability theorists and those used in
formal reasoning about probabilistic programs. On the one hand, a probability theorist …

Probabilistic programs often trade accuracy for efficiency, and thus may, with a small
probability, return an incorrect result. It is important to obtain precise bounds for the …

Separation logic's compositionality and local reasoning properties have led to significant
advances in scalable static analysis. But program analysis has new challenges—many …

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 …

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 …

We propose a semantic foundation for logics for reasoning in settings that possess a
distinction between equality of variables, a coarser equivalence of variables, and a notion of …

We present Bluebell, a program logic for reasoning about probabilistic programs where
unary and relational styles of reasoning come together to create new reasoning tools. Unary …

Properties such as provable security and correctness for randomized programs are naturally
expressed relationally as approximate equivalences. As a result, a number of relational …

Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy
in modern distributed applications. Currently, proof methods for low-level MPC protocols are …

We present ExpIris, a separation logic framework for the (amortized) expected cost analysis
of probabilistic programs. ExpIris is based on Iris, parametric in the language and the cost …