With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

We introduce and analyze stochastic optimization methods where the input to each update
is perturbed by bounded noise. We show that this framework forms the basis of a unified …

Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

We consider the classic correlation clustering problem: Given a complete graph where
edges are labelled either+ or−, the goal is to find a partition of the vertices that minimizes the …

In this paper, we study correlation clustering under fairness constraints. Fair variants of $ k $-
median and $ k $-center clustering have been studied recently, and approximation …

We introduce Saga, a next-generation knowledge construction and serving platform for
powering knowledge-based applications at industrial scale. Saga follows a hybrid batch …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …

We study correlation clustering in the streaming setting. This problem has been studied
extensively and numerous algorithms have been developed, most requiring multiple passes …

Correlation clustering is a central topic in unsupervised learning, with many applications in
ML and data mining. In correlation clustering, one receives as input a signed graph and the …

We study networks of communicating learning agents that cooperate to solve a common
nonstochastic bandit problem. Agents use an underlying communication network to get …