We study the problem of graph summarization. Given a large graph we aim at producing a
concise lossy representation (a summary) that can be stored in main memory and used to …

We present a new approach for solving (minimum disagreement) correlation clustering that
results in sublinear algorithms with highly efficient time and space complexity for this …

Correlation clustering is a fundamental optimization problem at the intersection of machine
learning and theoretical computer science. Motivated by applications to big data processing …

For text clustering, there is often a dilemma: one can either first embed each examples
independently and then compute pair-wise similarities based on the embeddings, or use a …

In correlation clustering, we are given $ n $ objects together with a binary similarity score
between each pair of them. The goal is to partition the objects into clusters so to minimise …

Given a set of objects and a pairwise similarity measure between them, the goal of
correlation clustering is to partition the objects in a set of clusters to maximize the similarity of …

Correlation clustering is arguably the most natural formulation of clustering. Given n objects
and a pairwise similarity measure, the goal is to cluster the objects so that, to the best …

Introduced in the mid-1970s as an intermediate step in proving a long-standing conjecture
on arithmetic progressions, Szemerédi's regularity lemma has emerged over time as a …

Consensus clustering (or clustering aggregation) inputs $ k $ partitions of a given ground set
$ V $, and seeks to create a single partition that minimizes disagreement with all input …

Computational learning theory studies the design and analysis of learning algorithms, and it
is integral to the foundation of machine learning. In the modern era, classical computational …