南宫28加拿大软件

光华讲坛——社ng28南宫国际app名流与企业家论坛第6985期

主题：Retrieval Learning for Heterogeneous Data Integration面向异构数据融合的检索学习方法

主讲人：美国加州大学圣塔芭芭拉分校统计与应用概率系 Annie Qu教授

主持人：统计与数据科学南宫28加拿大软件 常晋源教授

时间：7月15日16:00-17:00

地点：光华校区光华楼1003ng28南宫国际app议室

主办单位：统计与数据科学南宫28加拿大软件 国际交流与合作处 科研处

主讲人简介：

Annie Qu is Professor at Department of Statistics and Applied Probability, and Founding Director of Center for Statistical Foundations of Artificial Intelligence, University of California, Santa Barbara starting. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign during her tenure in 2008-2019, and Chancellor's Professor at UC Irvine in 2020-2025. She was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025, IMS Program Secretary from 2021 to 2027 and ASA Council of Sections of Governing Board Chair in 2025. She is the recipient of the 2025 Carver Medal of IMS, and 2026 ICSA Distinguished Achievement Awardee.

Annie Qu教授现任美国加利福尼亚大学圣塔芭芭拉分校统计与应用概率系教授、人工智能统计基础中心创始主任。1998年获宾夕法尼亚州立大学统计学博士学位。Qu教授长期致力于结构化与非结构化大规模复杂数据分析中的基础问题研究，重点发展面向复杂异构数据的前沿统计方法与理论，研究方向涵盖机器学习、精准医疗算法、文本挖掘、推荐系统、医学影像数据分析以及网络数据分析等领域。2008年至2019年，她任职于伊利诺伊大学厄巴纳-香槟分校，担任数据科学创始教授及伊利诺伊统计办公室主任；2020年至2025年担任加利福尼亚大学欧文分校Chancellor's Professor。Qu教授曾于2004—2009年获得美国国家科学基金（NSF）CAREER奖资助，是国际数理统计学ng28南宫国际appng28南宫国际app士、美国统计协ng28南宫国际appng28南宫国际app士以及美国科学促进ng28南宫国际appng28南宫国际app士。她于2024年获IMS Medallion Award并受邀担任IMS Medallion Lecturer；2023—2025年担任Journal of the American Statistical Association（JASA） Theory and Methods栏目联合主编，2021—2027年担任IMS项目秘书，2025年担任美国统计协ng28南宫国际app（ASA）Council of Sections Governing Board Chair。她还荣获2025年IMS Carver Medal和2026年国际华人统计协ng28南宫国际app（ICSA）杰出成就奖。

内容提要：

In the era of big data, large-scale, multi-modal datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariate shift, posterior drift, and missing modalities which can hinder the accuracy of existing prediction algorithms. To address these challenges, we propose a novel Representation Retrieval (R2) framework, which integrates a representation learning module (the representer) with a sparsity-induced machine learning model (the learner). Moreover, we introduce the notion of “integrativeness” for representers, characterized by the effective data sources used in learning representers, and propose a Selective Integration Penalty (SIP) to explicitly improve the property. Theoretically, we demonstrate that the R2 framework relaxes the conventional full-sharing assumption in multi-task learning, allowing for partially shared structures, and that SIP can improve the convergence rate of the excess risk bound. Extensive simulation studies validate the empirical performance of our framework, and applications to two real-world datasets further confirm its superiority over existing approaches.

在大数据时代，大规模、多模态数据广泛存在于医疗、金融和互联网等领域，为预测建模和科学发现提供了丰富的信息。然而，这类数据通常具有复杂的异质性，例如协变量分布偏移、后验分布漂移以及模态缺失等问题，给现有机器学习方法带来了巨大挑战，限制了模型的预测性能和泛化能力。针对这一问题，本次悟空体育将介绍一种新颖的表示检索（Representation Retrieval，R2）框架。该框架将表示学习与稀疏机器学习模型有机结合，通过学习具有良好表示能力的数据特征，实现来自不同数据源信息的有效融合。同时，提出了表示学习模型的“可整合性（integrativeness）”概念，并设计了选择性整合惩罚（Selective Integration Penalty，SIP）方法，以进一步提升异构数据融合的效果。理论分析表明，该框架突破了传统多任务学习中完全共享结构的假设，能够有效处理部分共享的数据结构，并进一步提高模型超额风险界的收敛速度。最后，本次悟空体育将结合模拟实验和两个真实数据集的分析结果，展示所提方法在复杂异构数据融合与预测任务中的优越性能。