Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …

Modern programmable blockchains have built-in support for smart contracts, ie ‍programs
that are stored on the blockchain and whose state is subject to consensus. After a smart …

We study the problem of learning controllers for discrete-time non-linear stochastic
dynamical systems with formal reach-avoid guarantees. This work presents the first method …

We present a new proof rule for proving almost-sure termination of probabilistic programs,
including those that contain demonic non-determinism. An important question for a …

We consider the quantitative problem of obtaining lower-bounds on the probability of
termination of a given non-deterministic probabilistic program. Specifically, given a non …

Probabilistic programs extend classical imperative programs with real-valued random
variables and random branching. The most basic liveness property for such programs is the …

The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the
program input result in proportional changes to the program output. For probabilistic …

Essential tasks for the verification of probabilistic programs include bounding expected
outcomes and proving termination in finite expected runtime. We contribute a simple yet …

Wir studieren die quantitative Analyse probabilistischer Programme. Dabei untersuchen wir
vornehmlich zwei Aspekte: Die Analysetechniken selbst, sowie die komplexitäts-bzw …

We consider the problem of expected cost analysis over nondeterministic probabilistic
programs, which aims at automated methods for analyzing the resource-usage of such …