arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.
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.
arXiv:2607. 28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models.
arXiv:2603. 24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.
arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.
arXiv:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.