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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
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: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.
arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.
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:2509. 07123v2 Announce Type: replace-cross Abstract: Generalized extreme value models capture dependence among choice alternatives in discrete choice modeling, but require this dependence to be predefined, symmetric, and shared uniformly across individuals.
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.