Hypergraph Self-Supervised Learning with Sampling-Efficient Signals
Abstract
We introduce initial models for abstract dialectical frameworks (ADFs) as a notion of minimal justifiable valuations and based on that, generalise the concept of serialisability of argumentation semantics to ADFs. In particular, we show that the characteristic operator-based semantics for ADFs can be characterised through serialisation sequences, which are, essentially, decompositions of a model into a series of initial models, representing a more fine-grained view into why a model is acceptable wrt. the semantics. We also analyse the computational complexity of tasks related to initial models.
Cite
Text
Li et al. "Hypergraph Self-Supervised Learning with Sampling-Efficient Signals." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/486Markdown
[Li et al. "Hypergraph Self-Supervised Learning with Sampling-Efficient Signals." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/li2024ijcai-hypergraph/) doi:10.24963/ijcai.2024/486BibTeX
@inproceedings{li2024ijcai-hypergraph,
title = {{Hypergraph Self-Supervised Learning with Sampling-Efficient Signals}},
author = {Li, Fan and Wang, Xiaoyang and Cheng, Dawei and Zhang, Wenjie and Zhang, Ying and Lin, Xuemin},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4398-4406},
doi = {10.24963/ijcai.2024/486},
url = {https://mlanthology.org/ijcai/2024/li2024ijcai-hypergraph/}
}