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论文提出 Geodesic-SVDD 框架，通过球面原型学习和测地线潜在空间 Mixup 实现单轮通信的分布式表格数据学习，在多个联邦基准上超越现有方法和联邦树集成。
提出联邦两步合成框架FITS，通过局部线性回归估计条件累积分布函数并结合continualization方法处理混合类型变量，在联邦设置下生成分布保真的表格合成数据。
INCONVENIENT 是一个用于评估 LLM 在便利采样偏差下泛化能力的基准，通过对训练分布中特征-目标关系进行可控扰动，涵盖 200 个表格数据集并生成约 15,500 个偏移训练分布，揭示了真实部署场景中的性能退化问题。
该论文研究了在表格数据上使用"类型化决策模型"（返回每个固定选项概率而非生成文本的语言模型），在行分类、事实验证和异常检测任务上与三个LLM对比：决策模型成本更低且在21种等义改写下更鲁棒，但所有模型对表格组织方式的改变都不鲁棒。
The paper introduces a leakage-aware framework for conversational task disambiguation over tabular data, decomposing agents into asking and solution policies, and presents AmbiTab, a benchmark suite unifying six ambiguous text-to-SQL datasets. It also trains an asking policy with reinforcement learning, improving disambiguation metrics on all six datasets and task success on five.
This paper proposes activation alignment, a lightweight linear transformation trained on synthetic unlabeled data to map partial-context student model activations toward full-context teacher activations, enabling faster tabular in-context learning with little performance loss. Evaluated on 38 TabArena datasets with TabPFN-3 and TabFM, the method recovers nearly half the teacher's advantage in low-data regimes while running on commodity hardware without a GPU.
This paper explores adapting prior-data fitted networks (PFNs) for tabular anomaly detection, proposing a fine-tuning-free approach (ZEN) using frozen TabPFN features with nearest-neighbor scoring, and a fine-tuned method (FOCUS) that surpasses all baselines on the ADBench benchmark.
This paper proposes a Fuzzy Cognitive Maps-based framework for generating interpretable synthetic medical tabular data with explicit causality and privacy preservation, achieving competitive TSTR performance (up to 0.81 accuracy, 0.90 AUROC on Heart Disease) against deep generative baselines while running entirely on CPU.
The paper proposes GenScript, an inference-only synthetic data pipeline that replaces per-dataset model training by passing a deterministic statistical profile of the source data to an LLM, which infers field semantics and integrity constraints that a coding agent then compiles into an auditable sampler. The approach unifies tabular, temporal, and relational data synthesis, building generators in minutes while closely matching leading methods in fidelity and preserving key integrity constraints.
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.