High Dimensional Inference with Random Maximum A-Posteriori Perturbations
This paper presents a new approach, called perturb-max, for high-dimensional statistical inference in graphical models that is based on applying random perturbations followed by optimization. This framework injects randomness into maximum a-posteriori (MAP) predictors by randomly perturbing the pote...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2021-01-11T18:36:43Z.
|
| Subjects: | |
| Online Access: | Get fulltext |