Distributed Parameter Estimation via Pseudo-likelihood

By Qiang Liu, A. Ihler. 2012

36 citations recorded by the source corpus.

Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental analysis. We show that simple linear combination or max-voting methods, when combined with second-order information, are statistically competitive with more advanced and costly joint optimization. Our algorithms have many attractive properties including low communication and computational cost and "any-time" behavior.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. A. Ihler
2. Qiang Liu
3. Michalis K. Titsias
4. Dilin Wang
5. Jun Zhu
6. Sergey Levine
7. Jiaxin Shi
8. Nicholas Zabaras

In Computer Networks and Communications

1. A. Ihler
2. Qiang Liu

Citations in the corpus

• Distributed Estimation, Information Loss and Exponential Families (2014, background): “An orthogonal setting of distributed learning is when the data is split across the variable dimensions, instead of the data instances; see e.g., Liu and Ihler (2012), Meng et al. (2013).”