Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

We present a statistical debugging algorithm that isolates bugs in programs containing
multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying …

Most real-world recommender services measure their performance based on the top-N
results shown to the end users. Thus, advances in top-N recommendation have far-ranging …

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely
examined on its own. With this practical book, you'll learn techniques for extracting and …

We propose a low-overhead sampling infrastructure for gathering information from the
executions experienced by a program's user community. Several example applications …

OBJECTIVE: To identify, survey, and systematically review the current knowledge regarding
obstetric fistula as a public health problem in low-income countries from the peer-reviewed …

We present a decision tree learning approach to diagnosing failures in large Internet sites.
We record runtime properties of each request and apply automated machine learning and …

The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for
identifying``authoritative''or``influential''articles, given hyperlink or citation information. That …

Automatic image annotation is a difficult and highly relevant machine learning task. Recent
advances have significantly improved the state-of-the-art in retrieval accuracy with …

The causes of performance changes in a distributed system often elude even its developers.
This paper develops a new technique for gaining insight into such changes: comparing …