We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

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 …

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic
devices, incorporating security into an SoC design flow poses significant challenges …

The performance of many machine learning methods depends critically on hyperparameter
settings. Sophisticated Bayesian optimization methods have recently achieved considerable …

State-of-the-art algorithms for hard computational problems often expose many parameters
that can be modified to improve empirical performance. However, manually exploring the …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …

The identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

Algorithm configuration (AC) is concerned with the automated search of the most suitable
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …