Tabular foundation models deliver fast accurate confidence intervals for data
Can Tabular Foundation Models Amortize Statistical Inference?
Machine Learning
Summary
Statistical inference usually requires designing special methods for each problem, which can be slow and complex. The authors created TabCon, a system that uses a type of machine learning model to quickly produce confidence intervals—ranges that likely contain true values—from different datasets. TabCon is much faster than traditional methods like bootstrapping, while still providing accurate and reliable results. It learns to do this through a smart design and additional training to ensure the confidence intervals are trustworthy.
What this means in practice
- •For data science teams: Produce reliable confidence intervals for new tabular datasets quickly without redesigning inference methods each time.
- •For business intelligence analysts: Speed up uncertainty quantification in decision-support reports by replacing slow resampling techniques with TabCon’s fast intervals.
Authors
Kai Ye, Shijin Gong, Hongyi Zhou, Valentina Zangirolami, Chengchun Shi
Abstract
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.