Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang
arXiv 26 May 2026 · Machine Learning
arXiv:2605.26559 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Han, Yafei and Calara Oereuran, Francisco and Ben-Akiva, Moshe and Z… (2022) A Neural-Embedded Discrete Choice Model: Learning Taste Representation with Strengthened Interpretability | 0.644 | 2 | 2 | 100% |
| 2 | Hollmann, Noah and Müller, Samuel and Purucker, Lennart and others (2025) Accurate Predictions on Small Data with a Tabular Foundation Model | 0.644 | 2 | 2 | 100% |
| 3 | Zhang, Xiyuan and Maddix, Danielle C and others (2025) Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models | 0.644 | 2 | 2 | 100% |
| 4 | Bierlaire, Michel and Axhausen, Kay W and Abay, Georg (2001) The Acceptance of Modal Innovation: The Case of Swissmetro | 0.511 | 2 | 1 | 100% |
| 5 | Ben-Akiva, Moshe E and Lerman, Steven R (1985) Discrete Choice Analysis: Theory and Application to Travel Demand | 0.405 | 1 | 1 | 100% |
| 6 | Hillel, Tim and Elshafie, Mohammed Z E B and Jin, Ying (2018) Recreating Passenger Mode Choice-Sets Generated by CAPI Interviews | 0.405 | 1 | 1 | 100% |
| 7 | Hillel, Tim and Bierlaire, Michel and Elshafie, Mohammed Z E B and J… (2021) A Systematic Review of Machine Learning Classification Methodologies for Modelling Passenger Mode Choice | 0.405 | 1 | 1 | 100% |
| 8 | Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff (2015) Distilling the Knowledge in a Neural Network | 0.405 | 1 | 1 | 100% |
| 9 | Hollmann, Noah and Müller, Samuel and Eggensperger, Katharina and Hu… (2023) TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second | 0.405 | 1 | 1 | 100% |
| 10 | Train, Kenneth E (2009) Discrete Choice Methods with Simulation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees | 0.405 | 1 | 1 |