Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Abstract
This work investigates training Conditional Random Fields (CRF) by Stochastic Dual Coordinate Ascent (SDCA). SDCA enjoys a linear convergence rate and a strong empirical performance for independent classification problems. However, it has never been used to train CRF. Yet it benefits from an exact line search with a single marginalization oracle call, unlike previous approaches. In this paper, we adapt SDCA to train CRF and we enhance it with an adaptive non-uniform sampling strategy. Our preliminary experiments suggest that this method matches state-of-the-art CRF optimization techniques.
Cite
Text
Le Priol et al. "Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields." Conference on Uncertainty in Artificial Intelligence, 2018.Markdown
[Le Priol et al. "Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields." Conference on Uncertainty in Artificial Intelligence, 2018.](https://mlanthology.org/uai/2018/priol2018uai-adaptive/)BibTeX
@inproceedings{priol2018uai-adaptive,
title = {{Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields}},
author = {Le Priol, Rémi and Piché, Alexandre and Lacoste-Julien, Simon},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2018},
pages = {815-824},
url = {https://mlanthology.org/uai/2018/priol2018uai-adaptive/}
}