Property testing is concerned with the design of super-fast algorithms for the structural
analysis of large quantities of data. The aim is to unveil global features of the data, such as …

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media,
etc., has opened up a large source for graph data. Graph embedding has been proved …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …

The aggressive optimization of floating-point computations is an important problem in high-
performance computing. Unfortunately, floating-point instruction sets have complicated …

Recently Rubinfeld et al.(ICS 2011, pp. 223–238) proposed a new model of sublinear
algorithms called local computation algorithms. In this model, a computation problem F may …

In this paper, we propose a distributed Newton method for decenteralized optimization of
large sums of convex functions. Our proposed method is based on creating a set of …

Given a directed graph G=(V,E) and an integer k≧1, a k-transitive-closure-spanner (k-TC-
spanner) of G is a directed graph H=(V,E_H) that has (1) the same transitive-closure as G …

The problem of monotonicity testing over the hypergrid and its special case, the hypercube,
is a classic question in property testing. We are given query access to f:[k] n-> R (for some …

Computer scientists have long believed that software is different from physical systems in
one fundamental way: while the latter have continuous dynamics, the former do not. In this …

This book introduces important results and techniques in property testing, where the goal is
to design algorithms that decide whether their input satisfies a predetermined property in …