Big Data can elicit greater insight, but storage or computational limitations -- or even privacy concerns -- challenge learning from massive data sets. Distributed algorithms fit such problems just right: they work on partial data and fuse intermediate results with local neighborhoods, over a distributed network of computing nodes. In this talk we will take a trip starting on GPS-denied localization as a motivation and culminating on a general distributed MAP inference algorithm for graphical models.

This presentation is part of Minisymposium “MS13 - Optimization for Imaging and Big Data (2 parts)”
organized by: Margherita Porcelli (University of Firenze) , Francesco Rinaldi (University of Padova) .

Authors:

Claudia Soares (LARSyS, Instituto Superior Técnico)

João Xavier (Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa)

João Gomes (Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa)

Keywords:

inverse problems, machine learning, map inference, nonlinear optimization, probabilistic graphical models