We consider active, semi-supervised learning in an offline transductive setting. We show that
a previously proposed error bound for active learning on undirected weighted graphs can be …

The rapid development of malicious software programs has posed severe threats to
Computer and Internet security. Therefore, it motivates anti-malware vendors and …

Network interdiction refers to disrupting a network in an attempt to either analyze the
network's vulnerabilities or to undermine a network's communication capabilities. A vast …

Optimization algorithms such as projected Newton's method, FISTA, mirror descent and its
variants enjoy near-optimal regret bounds and convergence rates, but suffer from a …

Active learning is a machine learning setting where the learning algorithm decides what
data is labeled. Submodular functions are a class of set functions for which many …

We introduce the concept of Most, and Least, Compact Spanning Trees-denoted
respectively by $ T^*(G) $ and $ T^\#(G) $-of a simple, connected, undirected and …

The efficient annotation of documents in vast corpora calls for scalable methods of text
classification. Representing the documents in the form of graph vertices, rather than in the …

Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great
deal of work has been devoted to designing defenses against attacks from malicious nodes …

Verfahren des überwachten maschinellen Lernens benötigen viele Trainingsbeispiele. Da
deren Beschaffung in der Fernerkundung aufwendig ist, wird ein dreistufiges Verfahren zur …

Node classification in attributed graphs is an important task in multiple practical settings, but
it can often be difficult or expensive to obtain labels. Active learning is an approach that aims …