NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:4699
Title:Graph Agreement Models for Semi-Supervised Learning

This paper proposed a novel graph learning method for graph-based semi-supervised learning. Besides the model of the classifier (the classification model in the paper), another model of the graph is considered (the agreement model in the paper), and the contribution to the loss of each edge is determined by the model of the graph. Although there are still concerns about the novelty in the end, we all agree that the proposed method is simple, well-explained and can still achieve good performance. This may have impacts to practitioners using semi-supervised learning in their projects, and as a result, I recommend an acceptance. Note that an important direction of related work is missing, namely, non-parametric graph learning methods for graph-based semi-supervised learning, see from NeurIPS 2012 and references therein (I am not a coauthor of any of them). Please survey this direction and include your survey in the final version.