统计与数据科学系系列学术报告之五百二十八期

题目：Can Tabular Foundation Models Amortize Statistical Inference? 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-of-experts 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.

Chengchun is an Associate Professor in the Department of Statistics at

LSE. He works at the interface of RL, LLMs and statistics, with applications

to ride-sharing and healthcare. His work brings to light the relevance

and significance of statistical learning in AI, and demonstrates the usefulness

of RL as a framework for policy evaluation and A/B testing in two-sided

marketplaces. His outstanding contributions have been recognized with

esteemed awards such as the Peter Gavin Hall IMS Early Career Prize, IMS

Tweedie Award and the Royal Statistical Society Research Prize. He has

served as the area editor of NeurIPS and associate editors of prestigious

journals JRSS-B, JASA and AoAS.