Kernel-nased just-in-time learning for passing expectation propagation messages
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classic...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Conference item |
| Published: |
Association for Uncertainty in Artificial Intelligence
2015
|