TIME: 1:30-2:20 pm, April 10, 2007 PLACE: CSE 403 SPEAKER: Anthony Wirth University of Melbourne TITLE: Improvements in consensus clustering and psychometric function estimation ABSTRACT: In this talk I will present the results of two projects: one on designing practical consensus clustering algorithms from approximation techniques, the other on improving psychometric function estimation. Both of these are outcomes of rewarding collaborations with a niche group of research-focused undergraduates at my institution. Consensus clustering is essentially the problem of finding the 1-median in the space of clusterings, provided there is a sensible metric on clusterings of the same data. Until Ailon et al's work a few years ago the trivial "pick-the-best-input-clustering" method was the only known approximation algorithm [factor 2]. Building on work of Gionis et al, Michael Bertolacci and I have shown that sampling the data, combined with good approximation and 'unsampling' methods yields high-quality consensuses quickly. Psychophysics is the study of how the response, or lack thereof, varies when a subject is shown different kinds of stimuli, or more often stimuli of varying intensities. The latter case is frequently modelled with a psychometric function, which somewhat resembles a cumulative distribution function. With Andrew Turpin (RMIT University), Elena Kelareva and James Mewing, we showed how to make a small improvement to an information-theory inspired method for estimating the threshold of a psychometric function. Bio: Tony Wirth was an undergraduate and masters student at the University of Melbourne, majoring in both Computer Science and Statistics. His PhD, completed in 2004, was with the theory group at Princeton University. He has been on the faculty of the Department of Computer Science and Software Engineering at Melbourne for just over two years.