arXiv Machine Learning August 4, 2026 By Zijian Shen, Taijie Chen, Bin Zhou, Ziyang Jiang, Jintao Ke

LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation

Read the original on arXiv Machine Learning →

arXiv:2608. 01879v1 Announce Type: new Abstract: Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 11

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

arXiv:2606. 11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult.

By Ruxue Shi, Yili Wang, Mengnan Du, Hangting Ye, Yi Chang, Xin Wang

llms benchmarks safety

arXiv AI

Jul 21

Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation

arXiv:2607. 17668v1 Announce Type: cross Abstract: Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution shifts.

By Ridong Han, Yawen Shen, Zhongnian Li, Tongfeng Sun, Xinzheng Xu, Abdulmotaleb El Saddik

fine-tuning efficiency safety

arXiv Machine Learning

Jun 2

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606. 00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer accessible.

By Yingxu Wang, Xinwang Liu, Siyang Gao, Nan Yin

fine-tuning benchmarks

arXiv Machine Learning

22h ago

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

arXiv:2605. 31156v2 Announce Type: replace Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.

By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye

llms benchmarks

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Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv Machine Learning

Jul 8

Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure

arXiv:2603. 10254v2 Announce Type: replace Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains.

By Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin, Giovanni Cin\`a

benchmarks safety data-engineering

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