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...
| Main Authors: | , , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery (ACM),
2015-12-18T14:39:52Z.
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| Subjects: | |
| Online Access: | Get fulltext |