QMC Working Group IV: Representative Points for Small-data and Big-data Problems

Group Leaders:
Roshan J. Vengazhiyil (Georgia Institute of Technology)
Simon Mak (Georgia Institute of Technology)

Weekly Meetings:
Meeting Times to be determined

Description:

Representative points, which compact a probability distribution into a finite point set, are useful for a wide array of “small-data” and “big-data” problems. Small-data problems arise naturally in many engineering applications, where a key challenge is to allocate limited experimental runs for performing functional approximation, uncertainty propagation or design optimization. Similarly, given the massive volume, variety and velocity of big-data (particularly in Bayesian problems), the reduction of such datasets using representative points allows for meaningful and timely analysis. This working group aims to investigate the theory and application of representative points to the aforementioned small-data and big-data problems, with an emphasis on engineering applications and Bayesian computation.

News and Updates:
Coming soon…

SAMSI Directorate Liaison: Ilse Ipsen

Questions: email qmc@samsi.info

Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics (QMC)