Tamir Hazan, Subhransu Maji, Tommi Jaakkola

University of Haifa; University of Massachusetts, Amherst; Massachusetts Institute of Technology

Poster: On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

7:00 – 11:59pm Thursday, December 05, 2013

Harrah's Special Events Center, 2nd Floor

This is part of the Poster Session which begins at 19:00 on Thursday December 5, 2013


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In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low dimensional perturbations and solving the corresponding MAP assignments. Our approach also leads to new ways to derive lower bounds on partition functions. We demonstrate empirically that our method excels in the typical "high signal - high coupling'' regime. The setting results in ragged energy landscapes that are challenging for alternative approaches to sampling and/or lower bounds.