Do Tabular Foundation Models Agree with Themselves?

• URL: http://arxiv.org/abs/2608.06004v1
• Date: Thu, 06 Aug 2026 13:09:54 GMT
• Title: Do Tabular Foundation Models Agree with Themselves?
• Abstract summary: We propose asking a different question: could a model's predictions result from any joint distribution?<n>The first is marginalization consistency, which demands that marginalized conditionals are equal to directly predicted marginals.<n>The second is factorization consistency, which demands that different factorization orders result in equal joint distributions.
• Score: 9.642976236410833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate the Bayesian posterior predictive distribution based on a pre-training prior. These univariate predictors can be converted into multivariate ones autoregressively by sampling one target and adding it to the features. However, the faithfulness of the resulting joint has not been investigated. Furthermore, TFMs cannot be evaluated against the posterior itself, at least not on real-world datasets, because the ground-truth distribution is unknown. We therefore propose asking a different question: could a model's predictions result from any joint distribution? To answer this question, we pose two requirements that any such model must satisfy. The first is marginalization consistency, which demands that marginalized conditionals are equal to directly predicted marginals. The second is factorization consistency, which demands that different factorization orders result in equal joint distributions. Every TFM that we evaluate violates both of these requirements for both classification and regression across all datasets.

Related papers

• Posterior Augmented Flow Matching [64.1559809786948]
  Posterior-Augmented Flow Matching (PAFM) is a generalization of flow matching (FM)<n>We show that PAFM improves over FM by up to 3.4 FID50K across different model scales.
  arXiv  Detail & Related papers  (2026-05-01T17:59:59Z)
• Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules [0.7009487789080344]
  We show that proper scoring rules induce different model rankings and different inductive biases during training.<n>Fine-tuning realTabPFNv2.5 with scoring rules not seen during pretraining yields consistent improvements on the corresponding metrics.
  arXiv  Detail & Related papers  (2026-03-09T10:38:01Z)
• Fixed-Horizon Self-Normalized Inference for Adaptive Experiments via Martingale AIPW/DML with Logged Propensities [0.0]
  Under adaptive assignment, propensities can keep changing, so the predictable quadratic variation of AIPW/DML increments may remain random.<n>We show that the Studentized statistic, with variance estimated by realized quadratic variation, is conditionally miscalibrated even without variance stabilization.
  arXiv  Detail & Related papers  (2026-02-17T13:12:31Z)
• Nearly Optimal Bayesian Inference for Structural Missingness [23.988531482641307]
  In the Bayesian view, prediction via the posterior predictive distribution integrates over the full model posterior uncertainty.<n>This framework decouples learning an in-model missing-value posterior from (ii) label prediction by optimizing the predictive posterior distribution.<n>It achieves SOTA on 43 classification and 15 imputation benchmarks, with finite-sample near Bayes-optimality guarantees.
  arXiv  Detail & Related papers  (2026-01-26T14:03:11Z)
• 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)
• Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost [53.746169882193456]
  Recent works have proposed various sparse attention modules to overcome the quadratic cost of self-attention. We propose a model that resolves both problems by endowing each attention head with a mixed-membership Block Model. Our model outperforms previous efficient variants as well as the original Transformer with full attention.
  arXiv  Detail & Related papers  (2022-10-27T15:30:52Z)
• On the Strong Correlation Between Model Invariance and Generalization [54.812786542023325]
  Generalization captures a model's ability to classify unseen data. Invariance measures consistency of model predictions on transformations of the data. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets.
  arXiv  Detail & Related papers  (2022-07-14T17:08:25Z)
• Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression [14.493176427999028]
  We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE) with linear regression models. We show that the T-learner fails to achieve the consistency except the random assignment, while the IPW-learner converges the risk to zero if the propensity score is known.
  arXiv  Detail & Related papers  (2022-02-10T18:51:52Z)
• Multivariate Probabilistic Regression with Natural Gradient Boosting [63.58097881421937]
  We propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
  arXiv  Detail & Related papers  (2021-06-07T17:44:49Z)
• Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification [11.651410633259543]
  We learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model. Experiments have been carried out on Market1501, DukeMTMC, MARS and noisy dataset as well.
  arXiv  Detail & Related papers  (2020-07-17T07:20:39Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

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.