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PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
May 13, 2024, 4:42 a.m. | Jaejun Lee, Minsung Hwang, Joyce Jiyoung Whang
cs.LG updates on arXiv.org arxiv.org
Abstract: While a number of knowledge graph representation learning (KGRL) methods have been proposed over the past decade, very few theoretical analyses have been conducted on them. In this paper, we present the first PAC-Bayesian generalization bounds for KGRL methods. To analyze a broad class of KGRL models, we propose a generic framework named ReED (Relation-aware Encoder-Decoder), which consists of a relation-aware message passing encoder and a triplet classification decoder. Our ReED framework can express at …
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