arXiv Machine Learning August 14, 2026 By Kacper Jurek, Wojciech Batko, Marek \'Smieja, Marcin Przewi\k{e}\'zlikowski

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

Read the original on arXiv Machine Learning →

arXiv:2605. 08519v2 Announce Type: replace Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular data in domains like medicine, finance, and science.

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

arXiv Machine Learning

Jun 24

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.

By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

Closing the Alignment-Maturity Gap in Federated Prototype Learning

arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).

By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas

rag benchmarks safety

Hugging Face Trending Papers

Jul 14

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?

Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approach for self-supervised learning (SSL): a pretraining stage on unlabeled data followed by a finetuning stage on labeled data.

computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jul 7

Self-Supervised Learning from Structural Invariance

arXiv:2602. 02381v2 Announce Type: replace Abstract: Joint-embedding self-supervised learning (SSL), the key paradigm for unsupervised representation learning from visual data, learns from invariances between semantically-related data pairs.

By Yipeng Zhang, Hafez Ghaemi, Jungyoon Lee, Shahab Bakhtiari, Eilif B. Muller, Laurent Charlin

rag efficiency

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 Machine Learning

Jun 26

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

By Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov, Mile Mitrovic, Dmitry Simakov

llms benchmarks

Related stories

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

Closing the Alignment-Maturity Gap in Federated Prototype Learning

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?

Self-Supervised Learning from Structural Invariance

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

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots