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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…
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…
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
Jean Honorio, Tommi S. Jaakkola
We propose maximum likelihood estimation for learning Gaussian graphical models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast to the commonly used Laplace…
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
Tommi S. Jaakkola, Michael I. Jordan
We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of…