The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …

Given an unsupervised outlier detection task on a new dataset, how can we automatically
select a good outlier detection algorithm and its hyperparameter (s)(collectively called a …

Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with
how it is often applied in practice. In a systematic review of Max-Cut and quadratic …

Algorithm selection (AS), selecting the algorithm best suited for a particular problem
instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This …

Today there exists no shortage of outlier detection algorithms in the literature, yet the
complementary and critical problem of unsupervised outlier model selection (UOMS) is …

Operations research and data mining already have a long-established common history.
Indeed, with the growing size of databases and the amount of data available, data mining …

The choice of a graph learning (GL) model (ie, a GL algorithm and its hyperparameter
settings) has a significant impact on the performance of downstream tasks. However …

How can we automatically select an out-of-distribution (OOD) detection model for various
underlying tasks? This is crucial for maintaining the reliability of open-world applications by …

In this work, we develop techniques for fast automatic selection of the best forecasting model
for a new unseen time-series dataset, without having to first train (or evaluate) all the models …

Satisfiability modulo theories (SMT) solvers implement a wide range of optimizations that are
often tailored to a particular class of problems, and that differ significantly between solvers …