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?