Tabular Foundation Models for Discrete Choice Estimation

• URL: http://arxiv.org/abs/2607.13314v2
• Date: Mon, 20 Jul 2026 04:24:10 GMT
• Title: Tabular Foundation Models for Discrete Choice Estimation
• Abstract summary: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.<n>We ask whether TFMs can be effectively applied to discrete choice, a central demand estimation framework in marketing and operations.<n>We propose a reformulation that encodes both choice-set dependence and individual heterogeneity within a row-based learning framework.
• Score: 8.167439804974467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation. We ask whether TFMs can be effectively applied to discrete choice, a central demand estimation framework in marketing and operations, and find that directly applying TFMs yields limited performance. The gap is structural: TFMs assume row-independent observations, whereas discrete choice is inherently set-valued and subject to persistent consumer preference heterogeneity. We propose a reformulation that encodes both choice-set dependence and individual heterogeneity within a row-based learning framework. Evaluated on a yogurt scanner panel, individual-level heterogeneity encoding is the dominant driver of predictive accuracy. The best reformulation outperforms hierarchical Bayesian estimation on both holdout log-likelihood and hit rate, running 16 times faster, a practical advantage for large-scale demand estimation. The advantage is largest in the medium-data regime (10--40 purchase occasions per consumer), where parametric Bayesian shrinkage most distorts estimates for atypical consumers. Fine-tuning on population choice data provides additional gains for consumers with shallow purchase histories, where in-context learning has limited individual-specific signal to condition on. These results establish a principled approach for applying foundation models to consumer choice problems more broadly.

Related papers

• Preference Data Selection for Mitigating the Alignment Tax in Large Language Models [52.18310687523229]
  BALIGN is a balanced data selection strategy that mitigates catastrophic forgetting while optimizing alignment efficacy.<n>By aggregating three key data-centric features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples.<n>Experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains.
  arXiv  Detail & Related papers  (2026-08-25T07:58:42Z)
• Selective Ensemble Based on Preference-Directed Multi-Objective Bandits [90.75513823660775]
  We formalize a sequential decision problem under partially specified linear preferences.<n>We then propose a preference-directed upper confidence bound (PrefUCB) algorithm.<n> Experiments on large pre-trained model selective ensemble tasks and online asset allocation under institutional mandates validate our method.
  arXiv  Detail & Related papers  (2026-06-20T07:52:46Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning [0.9099663022952496]
  Federated Learning enables collaborative model training across decentralized data sources without data transfer.<n>Averaging-based FL is limited by the presence of non-IID data, which negatively impacts convergence speed and final model accuracy.<n>We propose a proactive client selection framework that aims to find an optimal federation of clients whose combined data match utility and fairness requirements before training begins.
  arXiv  Detail & Related papers  (2026-05-20T10:00:33Z)
• Response Time Enhances Alignment with Heterogeneous Preferences [49.69696266152175]
  We show that augmenting preference datasets with a simple, secondary signal can restore identifiability of the population's average preference.<n>Our results bring promises and open up new opportunities for future data-collection pipelines.
  arXiv  Detail & Related papers  (2026-05-07T22:05:23Z)
• Evidence-based Distributional Alignment for Large Language Models [58.65469623911573]
  We propose Evi-DA, an evidence-based alignment technique that improves the fidelity and robustness of LLM-based distribution estimation.<n>Given a target country, Evi-DA retrieves related World Values Survey items and their answer distributions, predicts a coarse Welzel value signature for each option, and infers the country-conditioned answer distribution in a structured format.
  arXiv  Detail & Related papers  (2026-03-03T03:34:06Z)
• Robust Assortment Optimization from Observational Data [32.720761309403436]
  We propose a framework for data-driven assortment optimization that accounts for potential distributional shifts in customer choice behavior.<n>Our approach models potential preference shift from a nominal choice model that generates data and seeks to maximize worst-case expected revenue.
  arXiv  Detail & Related papers  (2026-02-11T09:57:16Z)
• Intuitionistic Fuzzy Sets for Large Language Model Data Annotation: A Novel Approach to Side-by-Side Preference Labeling [0.0]
  This paper introduces a novel framework based on intuitionistic fuzzy sets (IFS) for modeling and aggregating human preferences in large language models (LLMs)<n>Our approach captures not only the degree of preference but also the uncertainty and hesitation inherent in human judgment through membership, non-membership, and hesitation degrees.<n> Experimental validation on multiple datasets demonstrates that our IFS-based approach significantly improves annotation consistency, reduces annotator fatigue, and produces higher-quality preference data.
  arXiv  Detail & Related papers  (2025-05-30T04:20:00Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.<n>We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.<n>Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• On the estimation of discrete choice models to capture irrational customer behaviors [4.683806391173103]
  We show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data. An extensive set of experiments assesses the predictive accuracy of the proposed approach.
  arXiv  Detail & Related papers  (2021-09-08T19:19:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.