方匡南，廈門大學經濟學院統計學與數據科學系教授、博士生導師、耶魯大學博士后，廈門大學統計學與數據科學系副主任、廈門大學信用大數據與智能風控研究中心主任。入選了國家高層次青年人才、福建省高層次人才A類等。兼任全國工業統計教學研究會副會長、中國商業統計學會市場調查與教學研究分會副會長、中國現場統計研究會大數據統計分會副理事長等。主要研究方向為經濟管理統計、統計機器學習、金融大數據、健康醫療大數據等。共發表學術論文100多篇，其中在統計學和數據科學期刊Journal of the American Statistical Association, Journal of Machine Learning Research, Biometrics, Statistica Sinica, Bioinformatics, Journal of Computational and Graphical Statistics等發表論文50多篇，在經濟管理期刊Journal of Econometrics, Journal of Business & Economic Statistics, INFORMS Journal on Computing (UTD24), International Journal of Forecasting，《經濟研究》《統計研究》《管理科學學報》《數量經濟技術經濟研究》《世界經濟》等發表論文50多篇。多份研究成果被中辦、國辦等采用或獲領導批示。著有學術專著和教材等6部。主持了國家社科基金重大項目1項，國家自然科學基金項目4項，企事業橫向課題30多項。獲省部級以上科研成果獎9項，其中一等獎2項，二等獎2項。
報告摘要：Customer records include only customers in default (positive samples) and rejected customers (unlabeled samples), or positive and unlabeled (PU) data, which is a common scenario in emerging financial institutions. However, building credit scoring models using multiple small sample PU datasets with high dimensionality poses significant challenges, especially in light of the privacy constraints associated with transferring raw data. To tackle these challenges, this paper introduces a novel methodology called Covariate-aware Personalized federated PU learning. This approach utilizes a fused penalty function to automatically divide coefficients into multiple clusters, while an efficient proximal gradient descent algorithm is introduced for model training, relying solely on gradients from local servers. Theoretical analysis establishes the oracle property of our proposed estimator. The simulation results show that, in terms of variable selection, parameter estimation, and prediction performance, our method is close to the Oracle estimator and outperforms the other alternatives. Empirical results indicate that our method can improve prediction performance and facilitate the identification of heterogeneity across datasets.
