October 4, 2012: Machine Learning Seminar

Machine Learning Seminar

Thursday October 4, 2012
3:00 p.m., SEO 1000

Automatically Finding Performance Problems with Feedback-Directed Learning?Software Testing

Mark Grechanik

A goal of performance testing is to find situations?when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically.

We offer a novel solution for finding performance problems in applications automatically using black-box software testing. Our solution is an adaptive, feedback-directed learning testing system that learns rules from execution traces of applications and then uses these rules to select test input data automatically for these applications to find more performance problems when compared with exploratory random testing. We have implemented our solution and applied it to a medium-size application at a major insurance company and to an open-source application. Performance problems were found automatically and confirmed by experienced testers and developers.

Recent Work on Learning Theory

Dimitrios Diochnos

A brief overview of some of our work during the last 3 years. Part of the work deals with problems on Evolvability which is a restricted form of PAC (Probably Approximately Correct) Learning. Another part deals with halfspaces under the MIL (Multiple Instance Learning) setup, which is a variant of PAC Learning found in many interesting applications such as drug design and image classification. Time permitting we may also go through some additional problems emerging from the computer game of Heroes of Might and Magic III that are connected to, or combine elements of learning, reverse engineering, and software reengineering.

A Bird's Eye View of Persistent Homology

Jeremy Kun

Persistent homology is a data mining technique to reason about the topology of a finite data set. We explain the practical motivation for persistent homology and persistence frameworks, describe the mathematics from a (very) high level, and showcase an industrial application.

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