arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.
By Liu Liu, Dan Zhang
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.
By Easton Huch, Michael Keane
arXiv:2609.16952v1 Announce Type: cross
Abstract: Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products. Choice model tr...
By Jiajie Zhang, Yanqiu Ruan, Xiao Jin, Chung Piaw Teo
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
arXiv:2207. 12877v3 Announce Type: replace Abstract: Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework.
By Ali Aouad, Antoine D\'esir
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.
By Sheng Lun Christine Cao, Destenie Nock, Alex Davis