Time: 10:00-11:00, Friday, October 16, 2026

Venue: Guoshun Campus, Room 302, Starr Building

Speaker: Professor Jianqing Fan, Princeton University

Moderator: Professor Wen Yu, Department of Statistics and Data Science, School of Management, Fudan University

Topic: SMART Fine-tuning Factor Augmented Neural Lasso

Abstract: Fine-tuning is a widely used strategy for adapting pre-trained models to new tasks, yet its methodology and theoretical properties in high-dimensional nonparametric settings with variable selection have not yet been developed. We propose a source-model-augmented residual tuning (SMART) framework, which incorporates the pre-trained source model into the target learner and estimates only the residual target-specific component. The approach is widely applicable, from parametric and sparse models to neural networks and blackbox machine learning models. We focus on the development of fine-tuning factor-augmented neural Lasso, resulting in SMART-FAN-Lasso. This transfer-learning framework for high-dimensional nonparametric regression with variable selection simultaneously handles covariate and posterior shifts. We use a low-rank factor structure to manage high-dimensional dependent covariates and a residual tuning decomposition in which the target function is expressed as a function of the source model and other target-specific variables, thereby reducing the effective complexity of the target task. We derive minimax-optimal excess risk bounds for SMART-FAN-Lasso, characterizing the precise conditions, in terms of relative sample sizes and function complexities, under which fine-tuning yields statistical acceleration over single-task learning. Extensive numerical experiments across diverse covariate- and posterior-shift scenarios demonstrate that SMART-FAN-Lasso consistently outperforms standard baselines and achieves near-oracle performance even under severe target sample size constraints, empirically validating the derived rates.

Bio: Professor Jianqing Fan is a Chair Professor of Finance and Professor of Operations Research and Financial Engineering, and former Chair of the Department, at Princeton University. He is a Member of the U.S. National Academy of Sciences, a Foreign Member of the Royal Academy of Sciences of Belgium, an Academician of Academia Sinica, the founding Dean of the School of Data Science and of the Institute for Big Data at Fudan University, and a former President of the Institute of Mathematical Statistics and of the International Chinese Statistical Association. He received the 2000 COPSS Presidents' Award, the 2007 Morningside Gold Medal of Applied Mathematics, the 2013 Pao-Lu Hsu Prize of the International Chinese Statistical Association, the 2014 Guy Medal in Silver of the Royal Statistical Society, the 2018 Noether Distinguished Scholar Award of the American Statistical Association, the 2021 Le Cam Award of the Institute of Mathematical Statistics, the 2024 Frontiers of Science Award, and the 2025 Wald Award, the highest honor of the Institute of Mathematical Statistics. He is a Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the Society for Financial Econometrics (SoFiE). He has served as Editor-in-Chief of the Journal of the American Statistical Association and of the finance area of Management Science, and as former Editor of the Annals of Statistics, Probability Theory and Related Fields, the Journal of Econometrics, and the Journal of Business and Economic Statistics. His main research areas include high-dimensional statistics, artificial intelligence, machine learning, financial econometrics, and bioinformatics. He has authored four books and more than 300 articles in these fields and is among the most highly cited scholars in the world.