时 间：2026年11月9 日（周一）14：00 - 15：00
地 点：普陀校区理科大楼A1514室
报告人：史成春 伦敦政治经济学院 副教授
主持人：章迎莹 华东师范大学统计学院
摘 要：
For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians design a problem-specific estimator together with a procedure for quantifying its uncertainty. This paper proposes a different paradigm. We focus on a classical problem in statistical inference, confidence interval construction, and develop TabCon, an amortized inference system built on a tabular foundation model that produces confidence intervals for new datasets through a simple forward pass. The key methodological ingredients of TabCon are a sparse mixture-ofexperts architecture and reinforcement-learning-based post-training that calibrate the resulting confidence intervals to a desired coverage level. Across a wide range of benchmark datasets, TabCon attains near-nominal coverage while producing short confidence intervals. At inference time, it also offers considerably greater computational efficiency, running 50 times faster than the classical bootstrap procedure, even when the latter uses only 50 bootstrap samples.
报告人简介：
现任伦敦政治经济学院统计系副教授，曾在北卡罗来纳州立大学（North Carolina State University）获得统计学博士学位。他的研究主要集中在强化学习领域（Reinforcement Learning），特别是在策略评估（Policy Evaluation）、因果推断（Causal Inference）、半监督学习（Semi-Supervised Learning）等方面的应用与优化。史博士曾荣获Institute of Mathematical Statistics (IMS) Tweedie Award和Royal Statistical Society (RSS) Research Prize等奖项。