57 citations · 405 across the 34 of their papers we have counts for
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cs.LG2013★ 10 cited
On Measure Concentration of Random Maximum A-Posteriori Perturbations
Francesco Orabona, Tamir Hazan, Anand D. Sarwate +1
The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference and learning in high dimensional complex models. By maximizing a randomly pertu…
cs.LG2013★ 28 cited
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
Tamir Hazan, Subhransu Maji, Tommi Jaakkola
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 sa…