Research Interest

Data science; Machine Learning; Mathematical Statistics

Fund

中国人民大学决策咨询及预研委托项目学术成果征集，大数据与政府统计发展，2020-2021；

国家统计局重大统计专项，工业统计云建设的路径与方法研究，2019-2021；

工业互联网创新发展工程，AIdustry 工业互联网平台试验测试项目-高校合作课题，2018-2021；

中石油规划总院，加油站定价专题问题研究-非线性定价，2018-2019；

中石油规划总院，加油站定价专题问题研究，2017-2018；

中石油规划总院，加油站最优价格的稳健算法研究，2017-2018；

中石油规划总院，加油卡数据规律研究，2016-2017；

中石油规划总院，中国石油加油卡数据分析-客户分级方法，2015-2016；

国家自然科学基金青年科学基金项目(11501567), 二元分类评估方法——pAUC及拓展, 2016-2018；

国家重点研发计划，我国人群增龄过程中健康状态变化特点与规律的研究，2018-2023；

国家自然科学基金项目面上项目，基于非结构化数据的个人信用评价；2018-2022；

国家重点研发计划，高质高效的审判支撑关键技术及装备研究，2018-2021；

国家社会科学基金项目重大项目，巨灾保险的精算统计模型及其应用研究，2016-2021；

教育部人文社科项目基地重大项目，大数据时空微结构统计方法及经济运行与社会活动风险精确监测研究，2017-2020；

中国人民大学重大规划项目，互联网统计学研究，2017-2020；

中国人民大学，智能数据云与全面量化平台，2017-2018；

中共中央政法委，群众安全感调查结果调节系数研究，2014-2015；

Publications

First Author： 

10. Yang, H., Lu, K., Lv, X. and Hu, F. (2019), Two-Way Partial AUC and Its Properties, Statistical Methods in Medical Research, 28, 184-195. 

9. Yang, H. and Zhao, Y. (2018), Smoothed jackknife empirical likelihood for the one-sample difference of quantiles, Computational Statistics and Data Analysis, 120, 58-69. 

8. Yang, H. and Zhao, Y. (2017),Smoothed jackknife empirical likelihood for the difference of two quantiles, Annals of the Institute of Statistical Mathematics, 69, 1059–1073. 

7. Yang, H., Lu, K. and Zhao, Y. (2017), A nonparametric approach for partial areas under ROC curves and ordinal dominance curves, Statistica Sinica, 27, 357-371. 

6. Yang, H., Liu, S. and Zhao, Y. (2016), Jackknife empirical likelihood for linear transformation models with right censoring, Annals of the Institute of Statistical Mathematics, 68, 1095–1109. 

5. Yang, H. and Zhao, Y. (2015). Smoothed jackknife empirical likelihood inference for ROC curves with missing data. Journal of Multivariate Analysis, 140, 123–138. 

4. Yang, H., Yau, C. and Zhao, Y. (2014). Smoothed empirical likelihood inference for the difference of two quantiles with right censoring. Journal of Statistical Planning and Inference, 146, 95–101 

3. Yang, H. and Zhao, Y. (2013). Jackknife empirical likelihood for the difference of ROC curve, Journal of Multivariate Analysis, 115, 270–284. 

2. Yang, H. and Zhao, Y. (2012). Smoothed empirical likelihood for ROC curves with censored data. Journal of Multivariate Analysis, 109, 254-263. 

1. Yang, H. and Zhao, Y. (2012). New empirical likelihood inference for transformation model. Journal of Statistical Planning and Inference, 142(7), 1659-1668. 

Corresponding Author： 

16. Yang, H. (2023), Decision Tree for Locally Private Estimation with Public Data, Conference on Neural Information Processing Systems, (with Ma, Y., Zhang, H. and Cai, Y.）

15. Yang, H. (2023), Extrapolated random tree for regression, International Conference on Machine Learning, (with Cai, Y., Ma, Y. and Dong, Y.）

14. Yang, H. (2022), Allies or rivals? Spatial price competition in the Chinese retail gasoline market of inner Mongolia, Spatial Economic Analysis,  (with Yu, Y., Cui, J. and Huang, C.)

13. Yang, H. (2022), Under-bagging Nearest Neighbors for Imbalanced Classification, Journal of Machine Learning Research, 23(118):1−63. (with Hang, H., Cai, Y. and Lin, Z.）

12. Yang, H. (2022), MTRec: Multi-Task Learning over BERT for News Recommendation, Annual Meeting of the Association for Computational Linguistics, (with Bi, Qiwei and etc.) 

11. Yang, H. (2022), Exploring Coarse-grained Pre-guided Attention to Assist Fine-grained Attention Reinforcement Learning Agents, International Joint Conference on Neural Networks. (with Liu, H., Liu, Y. and He, H.).

10.Yang, H. (2022), A varying-coefficient regression approach to modeling the effects of wind speed on the dispersion of pollutants, Environmental and Ecological Statistics, 29, pages433–452. (with He, K. Wang, Y. and Su, W.) 

9. Yang, H. (2022), Linguistic Specificity and Stock Price Synchronicity, China Journal of Accounting Research, (with Zhao, W. and Zhou, H.). 

8. Yang, H. (2021), Boosting Few-Shot Abstractive Summarization with Auxiliary Tasks, International Conference on Information and Knowledge Management, (with Bi, Q. and Li, H. ） 

7. Yang, H. (2021), Enhancement of Flood Inundation Mapping by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Benchmark Sen1Floods11 Datasets, Remote Sensing, (with Bai, Y., Wu, W. and etc.） 

6. Yang, H. (2021), Augmented Abstractive Summarization with Document-Level Semantic Graph, Journal of Data Science, Volume 19, Issue 3, pp. 450–464. (with Bi, Q., Li, H. and Lu, K.） 

5. Yang, H. (2021), Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN, Journal of Data Science, Volume 19, Issue 1, pp. 126–141. (with Wang, X., Liu, X. and Lu, Z.）

4. Yang, H. (2020), Boosted Histogram Transform for Regression, International Conference on Machine Learning, (with Cai, Y., Hang, H. and Lin, Z.） 

3. Yang, H. (2020), Jackknife empirical likelihood inference for the Pietra ratio, Computational Statistics & Data Analysis, 91-101. (with Zhao, Y.) 

2. Yang, H. (2018), Jackknife empirical likelihood for the skewness and kurtosis，Statistics and Its Interface, 11, 709-719.（with Zhao, Y., Anna Moss and Yan Zhang） 

1. Yang, H. (2015), Jackknife empirical likelihood inference for the mean absolute deviation, Computational Statistics & Data Analysis, 91, 92-101. (with Zhao, Y. and Meng, X.) 

第一作者： 

2、基于即时预测方法的中间投入估算，统计研究,  2022年06期。 

1、中国输入性金融风险：测算、影响因素与来源，数量经济技术经济研究, 2020年07期。 

其他： 

9、市场会奖励数字化转型的“行胜于言”吗? 南开管理评论，已接收。

8、Dynamics of the gas retail market under China's price cap regulation，Energy Policy，174，2023.

7、重大突发公共卫生事件与国际金融风险溢出网络，国际金融研究，已接收。

6、跨境资本流动宏观审慎政策防范输入性金融风险机制研究，经济学家，2022年09期。

5、Nightlights as a Proxy of Economic Indicators: Fine-Grained GDP Inference Around Mainland China via Attention-Augmented CNN from Daytime Satellite Imagery, Remote Sensing, 2021. 

4、外部冲击类型与中国经济周期波动--兼论宏观审慎政策的有效性，国际金融研究，2021年03期。 

3、Technical solution discussion for key challenges of operational convolutional neural network-based building-damage assessment from satellite imagery: Perspective from benchmark XBD dataset，Remote Sensing, 12, 2020. 

2、Evaluation of driving risk at different speeds, Insurance Mathematics and Economics, 88, 108-119，2019. 

1、基于货币群落视角的人民币汇率全球溢出效应研究，国际金融研究，2018年09期。

Sofeware： 1. R package: tpAUC