In this review article we discuss connections between the physics of disordered systems,
phase transitions in inference problems, and computational hardness. We introduce two …

Publisher Summary The past few years have seen enormous progress in the performance of
Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known …

This paper proposes a new optimization algorithm called Entropy-SGD for training deep
neural networks that is motivated by the local geometry of the energy landscape. Local …

The problem of optimizing over random structures emerges in many areas of science and
engineering, ranging from statistical physics to machine learning and artificial intelligence …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

Neural networks are increasingly employed in safety-critical domains. This has prompted
interest in verifying or certifying logically encoded properties of neural networks. Prior work …

For many random constraint satisfaction problems, by now there exist asymptotically tight
estimates of the largest constraint density for which solutions exist. At the same time, for …

Among various algorithms designed to exploit the specific properties of quantum computers
with respect to classical ones, the quantum adiabatic algorithm is a versatile proposition to …

The binary (or Ising) perceptron is a toy model of a single-layer neural network and can be
viewed as a random constraint satisfaction problem with a high degree of connectivity. The …

We introduce a notion of\emph {generic local algorithm} which strictly generalizes existing
frameworks of local algorithms such as\emph {factors of iid} by capturing local\emph …