We study the problem of k-means clustering in the presence of outliers. The goal is to cluster
a set of data points to minimize the variance of the points assigned to the same cluster, with …

In this work, we study the problem of scheduling parallelizable jobs online with an objective
of minimizing average flow time. Each parallel job is modeled as a DAG where each node is …

Although concurrent data structures are commonly used in practice on shared-memory
machines, even the most efficient concurrent structures often lack performance theorems …

Hierarchical clustering is a fundamental tool in data mining, machine learning and statistics.
Popular hierarchical clustering algorithms include top-down divisive approaches such as …

In this paper we study the problem of scheduling a set of dynamic multithreaded jobs with the
objective of minimizing the maximum latency experienced by any job. We assume that jobs …

Hierarchical clustering is a widely used tool in machine learning, several sequential hierarchical
clustering algorithms are known and well-studied. These algorithms include top down …

In this paper, we consider scheduling parallelizable jobs online to maximize the throughput
or profit of the schedule. In particular, a set of n jobs arrive online and each job $$J_i$$ J i …

Many algorithms have been proposed to efficiently schedule parallel jobs on a multicore and/or
multiprocessor machine to minimize average flow time, and the complexity of the …

We introduce a new model for cooperative agents that seek to optimize a common goal
without communication or coordination. Given a universe of elements V, a set of agents, and a …

Average-linkage is one of the most popular hierarchical clustering algorithms. It is well known
that average-linkage does not scale to large data sets due to the slow asymptotic running …