arXiv AI By Liu Liu, Dan Zhang

Tabular Foundation Models for Discrete Choice Estimation

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arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.

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arXiv Machine Learning
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Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees

arXiv:2606. 26432v1 Announce Type: new 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 can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.

By Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang
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Bayesian Deep Learning for Discrete Choice

arXiv:2505. 18077v3 Announce Type: replace-cross Abstract: Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences.

By Daniel F. Villarraga, Ricardo A. Daziano
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Robust Assortment Optimization from Observational Data

The paper introduces a robust framework for assortment optimization that addresses distributional shifts in customer choice behavior. It demonstrates computational tractability when the nominal choice model is known and develops statistically optimal algorithms for the data‑driven setting, providing matching upper and lower bounds on sample complexity. The authors identify "robust item‑wise coverage" as the minimal data requirement for efficient robust learning, bridging robustness and statistical efficiency in assortment planning.

By Miao Lu, Yuxuan Han, Han Zhong, Zhengyuan Zhou, Jose Blanchet