Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields [article]

Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
2018 arXiv   pre-print
This work investigates the training of conditional random fields (CRFs) via the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical performance for binary classification problems. However, it has never been used to train CRFs. 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 CRFs, and we enhance it
more » ... with an adaptive non-uniform sampling strategy based on block duality gaps. We perform experiments on four standard sequence prediction tasks. SDCA demonstrates performances on par with the state of the art, and improves over it on three of the four datasets, which have in common the use of sparse features.
arXiv:1712.08577v2 fatcat:ocxs6k2jlzewzbvatauo4lwcey