Abstract:Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price sometimes increases predicted demand, and implied willingness-to-pay estimates are frequently negative or implausible. We propose a two-stage adapter that embeds foundation model predictions within a utility-maximization framework. In the first stage, we estimate a standard choice model whose parameters are constrained to obey economic theory. In the second stage, we freeze those parameters and train a correction term that incorporates the foundation model's predictions as additional information. The result is a model that inherits the foundation model's accuracy gains while guaranteeing monotonic price-demand relationships under policy perturbation and producing analytically computable trade-off measures. On two transportation datasets, the adapter recovers up to 13 percentage points of accuracy over a standard logit model while maintaining perfect economic consistency, something neither the raw foundation models nor conventional distillation achieve.
| Comments: | 5 pages, 1 table. Accepted at the FMSD Workshop, ICML 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Econometrics (econ.EM) |
| Cite as: | arXiv:2605.26559 [cs.LG] |
| (or arXiv:2605.26559v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26559 arXiv-issued DOI via DataCite (pending registration) |
From: Yingshuo Wang [view email]
[v1]
Tue, 26 May 2026 05:13:20 UTC (32 KB)
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