新研究揭示表格型上下文学习的记忆机制，为理解大模型基础行为提供新视角
arXiv:2606.31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.e., tabular foundation models leveraging in-context learning (ICL), ach…
用大模型为表格列自动生成任务感知的描述，NL2SQL等下游任务将更精准高效。
arXiv:2606.21685v1 Announce Type: cross Abstract: Generating accurate and informative column descriptions (e.g. "membership status of customers" for t…
研究者将LLM作为判别器评估合成表格数据，揭示“以假乱真”的潜在风险。
arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy…
LLM自动化特征工程新方法MedFeat，模型感知与可解释性驱动，提升临床表格预测性能。
arXiv:2603.02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering o…
TabPFN家族再添新成员：专为表格数据设计的文本编码器预训练方案，有望提升小样本表格预测性能。
arXiv:2606.04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numeric…
一篇关于加速表格数据基础模型预训练的前沿研究，提出创新策略大幅提升训练效率。
arXiv:2606.03681v1 Announce Type: new Abstract: Pretraining cost is a major bottleneck for research on tabular foundation models, slowing the iteratio…
ICML 2026新作，探讨如何为表格数据的上下文学习提供可操作的反事实解释，推动AI决策的公平与可追溯。
arXiv:2605.31272v1 Announce Type: new Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there …
利用表格基础模型TabPFN处理稀疏岩土数据，兼顾可解释性与不确定性量化，为工程建模提供新思路。
arXiv:2603.21033v2 Announce Type: replace-cross Abstract: Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where unce…
用LLM引导潜在扩散模型，保留合成表格数据的列间逻辑关系，提升数据真实性和可用性。
arXiv:2503.02161v3 Announce Type: replace Abstract: Synthetic tabular data are increasingly being used to replace real data, serving as an effective s…
TabH2O统一了表格数据的分类与回归，单模型单次前向传播，基于上下文学习高效预测。
arXiv:2605.18383v1 Announce Type: new Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in …
针对医疗健康表格数据，提出编排与评估合成数据的系统化框架，填补数据隐私与可用性平衡的空白。
arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but w…
新框架PIQL利用特权信息同时加速表格基础模型训练并提升泛化能力。
arXiv:2605.07799v2 Announce Type: replace-cross Abstract: Training foundation models is computationally intensive and often slow to converge. We intro…
系统性综述揭示合成表格健康数据评估的关键挑战与指南，为数据质量评估提供严谨框架
arXiv:2504.18544v3 Announce Type: replace-cross Abstract: Generating synthetic tabular health data is challenging, and evaluating their quality is equ…
联邦学习遇到特征空间异构的难题？这篇论文提出新方法，解决传统FedAvg在客户特征子集重叠少时信息传递差的问题。
arXiv:2605.16099v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative training across decentralized clients, but most method…