GP-ND adds a log-KL divergence penalty between a GP's predictive distribution and Gaussian blobs placed on negative data pairs, aiming to fit positive points while avoiding obstacles.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Obstacle-aware Gaussian Process Regression
GP-ND adds a log-KL divergence penalty between a GP's predictive distribution and Gaussian blobs placed on negative data pairs, aiming to fit positive points while avoiding obstacles.