TabMF：用表格基础模型学习数据集表示
TabMF: Learning Dataset Representations with Tabular Foundation Models
这篇论文把 TabICLv2 和 TabPFN-2.5 拿来当数据集编码器用，做数据集检索和分布偏移监测，做数据管理方向的朋友可以看看。
arXiv 论文提出 TabMF 框架，把预训练表格基础模型（TFM）改造成监督数据集的编码器。方法是在 TabICLv2 和 TabPFN-2.5 上加入数据集级 readout，并用合成监督数据集的对比后训练做端到端适配。在 6 项任务的真实数据集评测中，两个 TabMF 编码器在检索、精简基准构建、分布偏移严重度评估上取得最强综合表现。
TabMF: Learning Dataset Representations with Tabular Foundation Models
Representations of supervised datasets support higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. However, existing approaches typically rely either on hand-crafted meta-features or on dataset encoders trained specifically for representation learning. We introduce TabMF, a framework that repurposes pretrained tabular foundation models (TFMs) as encoders of supervised datasets. TabMF equips a predictive TFM with an architecture-aware dataset-level readout and adapts the model end-to-end using contrastive post-training on stochastic views of synthetic supervised datasets. We instantiate TabMF on TabICLv2 and TabPFN-2.5 and evaluate the resulting representations across six tasks on unseen real datasets. Simple frozen TFM readouts yield competitive representations, indicating that predictive pretraining already induces substantial dataset-level structure. The two TabMF encoders obtain the strongest aggregate performance among the evaluated learned and classical dataset representations, with particularly strong results in dataset retrieval, compact benchmark construction, and distribution-shift severity assessment.
