Semi-Supervised Learning

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Relatively Smart: A New Approach for Instance-Optimal Learning
Mar 2, 2026Shaddin Dughmi, Alireza F. PourPAC Learning Semi-Supervised Learning
University of Southern California · University of Waterloo
SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
Feb 26, 2026Simon Roschmann, Paul Krzakala, Sonia Mazelet +2Cross-Modal Learning Multimodal Embedding
Helmholtz Munich · Technical University of Munich · Munich Center for Machine Learning +3
Semi-supervised CAPP Transformer Learning via Pseudo-labeling
Feb 1, 2026Dennis Gross, Helge Spieker, Arnaud Gotlieb +3Transformer Pseudo-Labeling
1- Simula Research Laboratory, Oslo Norway · 2- National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory Greece
Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
Dec 11, 2025Tian Liu, Anwesha Basu, James Caverlee +1VLM Adaptation Few-Shot Learning
Texas A&M University · University of Macau · Institute of Collaborative Innovation
No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels
Aug 15, 2025Yiyang Shen, Weiran WangLearning with Missing Data Multi-View Learning
Department of Computer Science, University of Iowa, Iowa City, Iowa, USA
Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision
May 18, 2025Alexander Panyshev, Dmitry Vinichenko, Oleg Travkin +2Temporal GNNs Temporal Link Prediction
Risk AI Research Lab Moscow Independent Research Institute of Artificial Intelligence Moscow, Russia · Risk AI Research Lab Moscow, Russia · Risk AI Research Lab Applied AI Institute Moscow, Russia
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
Date pendingHongjie Zhu, Xiwei Liu, Rundong Xue +5Promptable Image Segmentation Semi-Supervised Medical Image Segmentation
SWUN · MBZUAI · XJTU +2

Mar 2, 2026, cs.LGJ/K move · Enter open · S save

Relatively Smart: A New Approach for Instance-Optimal Learning

Shaddin Dughmi, Alireza F. Pour

University of Southern California · University of Waterloo

PAC Learning Semi-Supervised Learning Statistical Learning Theory

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We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data. Prior work has shown that such marginal-by-marginal guarantees are possible for "most" marginals, with respect to an arbitrary fixed and known measure, but not more generally. We discover that this failure can be attributed to an "indistinguishability" phenomenon: There are marginals which cannot be statistically distinguished from other marginals that require different learning approaches. In such settings, semi-supervised learning cannot certify its guarantees from unlabeled data, rendering them arguably non-actionable. We propose relatively smart learning, a new framework which demands that a supervised learner compete only with the best "certifiable" semi-supervised guarantee. We show that such modest relaxation suffices to bypass the impossibility results from prior work. In the distribution-free setting, we show that the One-Inclusion Graph learner is relatively smart up to squaring the sample complexity, and show that no supervised learning algorithm can do better. For distribution-family settings, we show that relatively smart learning can be impossible or can require idiosyncratic learning approaches, and its difficulty can be non-monotone in the inclusion order on distribution families.

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