Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method

Abstract

We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we… (More)

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Cite this paper

@inproceedings{Zhang2017RerevisitingLO, title={Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method}, author={Chenzi Zhang and Shuguang Hu and Zhihao Gavin Tang and T.-H. Hubert Chan}, booktitle={ICML}, year={2017} }