Xiaolin Gui — accepted AI-conference papers

2022

Federated Learning with Positive and Unlabeled Data

Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng +1

ICML 2022spotlight

We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time. Different from the settings in traditional PU learning where the negative class consists of a sin…

Cited by 29 Source ↗PDF ↗code ↗Scholar ↗

2021

Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking

Yingjie Gu, Xiaoye Qu, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Xiaolin Gui

AAAI 2021technical

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framew…

Cited by 32 Source ↗PDF ↗Scholar ↗