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This paper introduces a pipeline that uses transformers and embeddings to extract interpretable decision rules from clinical tabular data, translating latent biomarkers back to measurable features for better clinical decision support.
This paper introduces OpTFM, a comparative evaluation framework for tabular foundation models in healthcare, assessing models across six clinically meaningful dimensions like generalization and fairness, and applies it to two use cases to demonstrate context-dependent rankings.
MetaRTL is a two-stage framework for relational table learning that uses meta-path attention to efficiently capture relational semantics, demonstrated effective across multiple datasets.
This paper presents an interpretable machine learning approach using a naive Bayes classifier on a small clinical dataset to predict cognitive impairment from inflammatory biomarkers, identifying I-309 (CCL1) as a key predictive feature.
SCTab-Diff is a semantics-consistent tabular diffusion framework that uses weak semantic priors to generate high-fidelity synthetic tabular data, improving distributional fidelity and semantic consistency over existing methods.
TabPFN-3.5 has been released, achieving top performance on tabular benchmarks including TabArena and BeyondArena, and enables industry use cases for text columns, grouped data, wide tables, and high cardinality data.
A controlled study finds that feature engineering gains diminish for stronger tabular foundation models, while adding in-context information from related datasets still improves performance.
LimiX-2 is a pretrained foundation model for structured data that uses contextual mechanism networks to achieve #1 on major tabular benchmarks, supporting multiple tasks without task-specific parameter updates.
This paper investigates scaling laws in actuarial ratemaking models using tabular data, finding that TabM exhibits stronger data scaling than standard Transformer and MLP architectures without additional inductive biases.
Introduces Xiaomi-TabLDM, a tabular foundation model that leverages synthetic data and in-context learning for superior prediction accuracy without task-specific fine-tuning, achieving top rankings on multiple benchmarks.
Counterfactual Fragility Certificates (CFC) introduce a model-agnostic audit protocol to detect high-confidence brittleness in machine learning models under structured evidence failure scenarios, improving over existing methods.
This paper proposes the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a decision tree model that improves classification accuracy under depth constraints through adaptive multi-way splits, achieving superior performance on OpenML benchmarks.
Entropic Scree is a new diagnostic tool for assessing signal strength and structure in dirty tabular data using mutual information, with a preprint and upcoming Python and R packages.
This paper introduces ClusterAttention, a training-free method to speed up bidirectional attention in transformers by using recursive clustering for block-sparse attention, achieving 2-6x speedups on tabular data and 1.8x on video generation while maintaining high accuracy.
This paper evaluates self-supervised pretraining for tabular models under label scarcity and missing data, finding mixed efficacy but reliable improvements under test-time missingness.
Tydra is a hybrid Transformer-SSM architecture for tabular data that reduces inference time by 30% compared to TabPFN while maintaining similar predictive performance and outperforming Hydra.
This paper investigates unsupervised anomaly detection using flow matching on tabular data, focusing on contaminated training sets and comparing different scoring methods for robustness.
The paper proposes LLM-Detector, a framework that uses large language models with in-context learning to perform tabular anomaly detection without fine-tuning, demonstrating consistent improvements over existing methods on multiple datasets.
The Entropic Scree is an information-theoretic upgrade to PCA for robust rank estimation and dimensionality reduction on messy tabular data, with open-sourced code and a preprint released.
The Entropic Scree is a non-parametric, information-theoretic diagnostic framework that maps intrinsic rank and informational gravity in complex tabular data, addressing limitations of standard PCA and other baselines.