Nowadays, many computer and communication systems generate graph data. Graph data
span many different domains, ranging from online social network data from networks like …

Learning from Label Proportions (LLP) is a learning setting, where the training data is
provided in groups, or" bags", and only the proportion of each class in each bag is known …

We describe a half-approximation algorithm, b-Suitor, for computing a b-Matching of
maximum weight in a graph with weights on the edges. b-Matching is a generalization of the …

Multimedia applications are an increasingly important workload for general-purpose
processors. This paper analyzes frame-level execution time variability for several multimedia …

We survey recent work on approximation algorithms for computing degree-constrained
subgraphs in graphs and their applications in combinatorial scientific computing. The …

\ldp deployments are vulnerable to inference attacks as an adversary can link the noisy
responses to their identity and subsequently, auxiliary information using the\textit {order} of …

We consider supervised learning with random decision trees, where the tree construction is
completely random. The method is popularly used and works well in practice despite the …

Syntactic data anonymization strives to (i) ensure that an adversary cannot identify an
individual's record from published attributes with high probability, and (ii) provide high data …

A b-MATCHING is a subset of edges M such that at most b (v) edges in M are incident on
each vertex v, where b (v) is specified. We present a distributed-memory parallel algorithm, b …

We describe a 3/2-approximation algorithm, LSE, for computing ab-Edge Cover of minimum
weight in a graph with weights on the edges. The b-Edge Cover problem is a generalization …