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This paper proposes a reformulation to apply tabular foundation models (TFMs) to discrete choice estimation, addressing the structural gap of row-independent assumptions. The best reformulation outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate while running 16 times faster.
This paper investigates whether tabular in-context learning models, pretrained on synthetic causal tables, can generalize to predict biomolecular properties from limited labeled data. The authors find that these models are competitive for protein fitness regression but that representation choice is crucial for small-molecule classification.
This paper presents a formal impossibility result showing that tabular foundation models cannot distinguish legal from rule-violating database states without access to operational rules, verified through the Operational Turing Test (OTT) and empirical experiments.
This paper evaluates the robustness of tabular foundation models to biologically inspired distribution shifts in microbiome data, finding that protecting discriminative features is insufficient and zero-imputation is the most harmful perturbation.
This paper proposes CRUMB, a three-stage inference wrapper that clusters test queries and selects a distributionally matched training subset via MMD minimization to enable efficient Prior-Fitted Network inference on large datasets, achieving state-of-the-art context selection on 51 TabArena datasets.
This paper introduces GOTabPFN, a method that combines Graph-guided Ordering with Local Refinement (GO-LR) and Neuro-Inspired Subunit Compression (NSC) to make small tabular foundation models effective for high-dimensional, low-sample-size prediction without retraining large backbones.
This paper proposes a domain-aware coreset construction pipeline that enables a tabular foundation model to predict flood depth with only 0.7% of the training data, achieving 98.5% of the supervised reference accuracy and allowing transfer across watersheds without retraining.
This paper evaluates the use of tabular foundation models, particularly TabPFN, for calibrating near-infrared spectroscopy data. The model shows strong performance on regression and classification tasks compared to traditional chemometric methods.