Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
In a sampling problem, we are given an input x {0, 1} n , and asked to sample approximately from a probability distribution Dx over poly (n)-bit strings. In a search problem, we ...
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two ex...
Martin Pelikan, Jiri Ocenasek, Simon Trebst, Matth...
We consider in this paper a class of Publish-Subscribe (pub-sub) systems called topic-based systems, where users subscribe to topics and are notified on events that belong to thos...