519 citations · 2.4k across the 48 of their papers we have counts for
15 papers · 1 filter
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Shaohua Fan, Xiao Wang, Chuan Shi +2
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization…
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-…
ListReader: Extracting List-form Answers for Opinion Questions
Peng Cui, Dongyao Hu, Le Hu
Question answering (QA) is a high-level ability of natural language processing. Most extractive ma-chine reading comprehension models focus on factoid questions (e.g., who, when, w…
Topic-Guided Abstractive Multi-Document Summarization
Peng Cui, Le Hu
A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we repre…
Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu, Zheyan Shen, Yue He +4
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Inde…