Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

The development of practical approaches to efficiently reasoning over pseudo-Boolean
constraints has recently increasing attention as a natural generalization of Boolean …

Recent developments in applying and extending Boolean satisfiability (SAT) based
techniques have resulted in new types of approaches to pseudo-Boolean optimization …

Understanding causal relationships is a central challenge in many research endeavours.
Recent research has shown the importance of accounting for feedback (cycles) and latent …

In this paper, we proposed a novel quantum algorithm for the maximum satisfiability
problem. Satisfiability (SAT) is to find the set of assignment values of input variables for the …

Discovering graphical models over very general model spaces with high accuracy requires
optimally combining conflicting (in) dependence constraints in sample data, and thus results …

The study of causality has recently gained traction in computer science. Formally capturing
causal reasoning would allow computers to answer “Why”-questions and would result in …

We consider causal discovery in a very general setting involving non-linearities, cycles and
several experimental datasets in which only a subset of variables are recorded. Recent …

We propose a new optimization-based method for learning causal structures from
observational data, a process known as causal discovery. Our method takes as input …