Dear Friends,
Hope you had a great summer! I am pleased to announce that our SLDS August webinar will be held on Aug 25, 1 pm Eastern Time, featured by Dr. Annie Qu from UCSB. Hope to see you there!
Title: Representation Retrieval Learning for Heterogeneous Data Integration
Speakers: Dr. Annie Qu, University of California Santa Barbara
Date and Time: August 25, 2026, 1:00 to 2:30 pm Eastern Time
Registration Link: ASA SLDS Webinar Registration Link [eventbrite.com]
Abstract:
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.
Presenter:
Annie Qu is Professor of Statistics and Applied Probability at the University of California, Santa Barbara, and Founding Director of the Center for Statistical Foundations of AI (CSFAI). Her research develops statistical foundations for artificial intelligence, with interests spanning statistical learning, heterogeneous data integration, reinforcement learning, causal inference, trustworthy AI, uncertainty quantification, precision health, and multimodal data analysis. She is an ASA Fellow, IMS Fellow, AAAS Fellow, and an elected member of the International Statistical Institute (ISI). She received the IMS Medallion Award (2024), the IMS Harry Carver Medal (2025), and the ICSA Distinguished Achievement Award (2026) in recognition of her contributions to statistics, machine learning, and artificial intelligence. She currently serves as Co-Editor of the Journal of the American Statistical Association (Theory and Methods).
------------------------------
Boxiang Wang
Associate Professor
Department of Statistics and Actuarial Science
University of Iowa
Iowa City, IA, United States
------------------------------