On measure concentration of random maximum a-posteriori perturbations

On measure concentration of random maximum a-posteriori perturbations

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 perturbed potential function, MAP perturbations generate unbiased samples from the Gibbs distribution. Unfortunately, the com...

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Bibliographic Details
Main Authors: Orabona, Francesco (Author), Hazan, Tamir (Author), Sarwate, Anand D. (Author), Jaakkola, Tommi S. (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2015-12-18T14:39:52Z.
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