Events Calendar

Machine Learning Center and Ming Hsieh Institute Series on Mathematical Foundations of Learning from Data and Signals Joint Seminar

Speaker: Sewoong Oh, University of Illinois Urbana-Champaign

Talk Title: Fundamental Limits and Efficient Algorithms in Adaptive Crowdsourcing

Series: MHI

Abstract: Adaptive schemes, where tasks are assigned based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently allocate the budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we propose a new model for representing practical crowdsourcing systems, which strictly generalizes the popular Dawid-Skense model, and characterize the fundamental trade-off between budget and accuracy. We introduce a novel adaptive scheme that matches this fundamental limit. We introduce new techniques to analyze the spectral analyses of non-back-tracking operators, using density evolution techniques from coding theory.

Biography: Sewing Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. He was co-awarded the Kenneth C. Sevcik outstanding student paper award at the Sigmetrics 2010, the best paper award at the SIGMETRICS 2015, and NSF CAREER award in 2016.

Host: Mahdi Soltanolkotabi

More Information: Oh Seminar Announcement.png

Location: Henry Salvatori Computer Science Center (SAL) - 101

Audiences: Everyone Is Invited

Contact: Gloria Halfacre