Advances in Partial-Label Learning: Theories, Algorithms, and Applications
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 18
Special Issue Editor
Special Issue Information
Dear Colleagues,
Partial-label learning (PLL) addresses the challenge of learning from ambiguously labeled data, where each instance is annotated with a set of candidate labels but only one is correct. As labeling costs rise and real-world data become increasingly uncertain, PLL has emerged as an important paradigm in weakly supervised learning.
This Special Issue aims to bring together cutting-edge research and new insights into the theory, methodology, and application of PLL. Topics include both foundational advances—such as unbiased risk estimation, label disambiguation, and theoretical generalization analysis—and practical developments—such as deep PLL models, multimodal PLL, continual and federated PLL, and integration with large language models (LLMs). We also welcome studies exploring connections between PLL and related paradigms such as semi-supervised learning, label noise learning, self-training, and weak supervision.
By gathering diverse perspectives from academia and industry, this Special Issue seeks to advance the understanding of PLL, inspire novel approaches to weak supervision, and foster real-world applications across domains like computer vision, natural language processing, recommendation, and healthcare.
We encourage submissions addressing, but not limited to, the following topics:
- Theoretical foundations of partial-label learning;
- Deep architectures and optimization strategies for PLL;
- Partial-label learning with large language models (LLMs);
- Multimodal partial-label learning;
- Continual and lifelong partial-label learning;
- Federated and privacy-preserving PLL;
- Partial-label learning under distribution shift and open world;
- Connections to weak supervision and noisy label learning;
- Explainability and uncertainty in partial-label models;
- Applications and benchmarks for PLL in the era of foundation models.
Dr. Beibei Li
Guest Editor
Manuscript Submission Information
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Keywords
- partial-label learning
- weak supervision
- deep learning
- large language models
- multimodal learning
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