Archive for the ‘GaBP’ Category

The quiet rise of Gaussian Belief Propagation (GaBP)

Sunday, August 7th, 2011

The quiet rise of Gaussian Belief Propagation (GaBP) by Danny Bickson.

From the post:

Gaussian Belief Propagation is an inference method on a Gaussian graphical model which is related to solving a linear system of equations, one of the fundamental problems in computer science and engineering.  I have published my PhD thesis on applications of GaBP in 2008.

When I started working on GaBP, it was absolutely useless algorithm with no documented applications.

Recently, I am getting a lot of inquiries from people who applying GaBP on real world problems. Some examples:

  • Carnegie Mellon graduate student Kyung-Ah Sohn, working with Eric Xing, is working on regression problem for finding causal genetic variants of gene expressions, considered using GaBP for computing matrix inverses.
  • UCSC researcher Daniel Zerbino using suing GaBP for smoothing genomic sequencing measurements with constraints.
  • UCSB graduate student Yun Teng is working on implementing GaBP as part of the KDT (knowledge discovery toolbox package).

Furthermore, I was very excited to find out today from Noam Koenigstein, a Tel Aviv university graduate about Microsoft Research Cambridge project called MatchBox, which is using Gaussian BP for collaborative filtering and being actually deployed in MS. Some examples to other conversations I had are:

  • Wall Street undisclosed company (that asked to remain private) who is using GaBP for parallel computation of linear regression of online stock market data.
  • A gas and oil company was considering to use GaBP for computing the main diagonal of the inverse of a sparse matrix.

The MatchBox project is a recommender system that takes user choices into account, even ones in a current “session.”

Curious, to what extent are user preferences the same or different from way they identify subjects and the subjects they would identify?