Abstract: The distance between two data points is a fundamental ingredient of many data analysis methods. In this talk I review some of my work on “human computation” algorithms that use relative distance comparisons only. I.e., statements of the form “of items a, b, and c, item c is an outlier”, or “item a is closer to item b than to item c”. Such statements are easier to elicit from human annotators than absolute judgements of distance. I consider the problems of centroid computation (Heikinheimo & Ukkonen, HCOMP 2013), density estimation (Ukkonen et al, HCOMP 2015), embeddings (Amid & Ukkonen, ICML 2015), clustering (Ukkonen, ICDM 2017), as well as give a few sneak previews of ongoing work.

Bio: Antti Ukkonen is an Academy research fellow at University of Helsinki. He obtained his doctoral degree at Aalto university in 2008, and has since held positions at Yahoo! Research, Helsinki Institute for Information Technology HIIT, and Finnish Institute of Occupational Health. Currently he is the PI in the “Data Science for the Masses” project funded by Academy of Finland. His research interests include algorithmic aspects of (distributed) human computation and machine learning, as well as applied data science.

Speaker: Antti Ukkonen

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki