Learning efficient random maximum a-posteriori predictors with non-decomposable loss functions
In this work we develop efficient methods for learning random MAP predictors for structured label problems. In particular, we construct posterior distributions over perturbations that can be adjusted via stochastic gradient methods. We show that every smooth posterior distribution would suffice to d...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Neural Information Processing Systems
2015
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| Online Access: | http://hdl.handle.net/1721.1/100402 https://orcid.org/0000-0002-2199-0379 |