In this survey we overview the definitions and methods for graph clustering, that is, finding
sets of “related” vertices in graphs. We review the many definitions for what is a cluster in a …

Optimization is a powerful paradigm for expressing and solving problems in a wide range of
areas, and has been successfully applied to many vision problems. Discrete optimization …

We study fairness in classification, where individuals are classified, eg, admitted to a
university, and the goal is to prevent discrimination against individuals based on their …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

An important part of our information-gathering behavior has always been to find out what
other people think. With the growing availability and popularity of opinion-rich resources …

Many tasks in computer vision involve assigning a label (such as disparity) to every pixel. A
common constraint is that the labels should vary smoothly almost everywhere while …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

A fundamental challenge in develo** high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

We address the rating-inference problem, wherein rather than simply decide whether a
review is" thumbs up" or" thumbs down", as in previous sentiment analysis work, one must …