Search Results for author: Yanru Qu

Found 14 papers, 10 papers with code

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

no code implementations16 Aug 2021 Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Wei-Wei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning

To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.

Diabetes Prediction Multi-Task Learning

Event Time Extraction and Propagation via Graph Attention Networks

1 code implementation NAACL 2021 Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth

Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.

Graph Attention Natural Language Understanding +2

GIKT: A Graph-based Interaction Model for Knowledge Tracing

3 code implementations13 Sep 2020 Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Wei-Nan Zhang, Yong Yu

With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.

Knowledge Tracing

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

1 code implementation12 Aug 2019 Yanru Qu, Ting Bai, Wei-Nan Zhang, Jian-Yun Nie, Jian Tang

This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems.

Click-Through Rate Prediction Knowledge Graphs

Dynamically Fused Graph Network for Multi-hop Reasoning

1 code implementation ACL 2019 Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei LI, Wei-Nan Zhang, Yong Yu

However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.

Question Answering

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

no code implementations12 Sep 2018 Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Wei-Nan Zhang, Ken Chen, Shaodian Zhang, Yong Yu

TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.

Graph Embedding Link Prediction

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

7 code implementations1 Jul 2018 Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

Click-Through Rate Prediction Feature Engineering +3

Wasserstein Distance Guided Representation Learning for Domain Adaptation

8 code implementations5 Jul 2017 Jian Shen, Yanru Qu, Wei-Nan Zhang, Yong Yu

Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).

Domain Adaptation General Classification +2

Product-based Neural Networks for User Response Prediction

10 code implementations1 Nov 2016 Yanru Qu, Han Cai, Kan Ren, Wei-Nan Zhang, Yong Yu, Ying Wen, Jun Wang

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.

Click-Through Rate Prediction Recommendation Systems

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