Algorithms Seminar/Fall10
From ResearchWiki
Modelling Data With Uncertainty
Wed 1:25-2:45pm
MEB 3147 (LCR)
Contents |
Synopsis
We will cover many recent developments in the modeling and processing of uncertain data in computer science. First we will overview different models of uncertainty, tracing their origins and motivation (Many-world models in databases, imprecision models in geometry, probabilistic models in databases). Then we will proceed to survey recent developments in computational geometry, databases, and machine learning and statistics. Highlights in computational geometry will describe algorithmic analysis to answer basic geometry problems, either with respect to worst case bounds or by formally approximating the distributions. Work in databases has focused on data structures to quickly retrieve summary queries of large data sets with respect to the underlying data uncertainty. This topic has been studied for longer in machine learning and statistics so we will summarize classic techniques and see some recent developments. Through this seminar participants will gain an understanding of the state-of-the-art in modeling and processing uncertain data and will be exposed to several important open problems and exciting research directions.
Schedule
(subject to change)
