A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
arXiv:2511.22823v2 Announce Type: replace Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire. However, many existing methods are tailored to specific supervision patterns -- such as positive-unlabeled (PU), unlabeled-unlabeled (UU), complementary-label (CLL), partial-label (PLL), or similarity-unlabeled annotations -- and rely on post-hoc corrections to mitigate instability induced by indirect supervision. We propose a principled, unified framework that bypasses such post
The increasing complexity and cost of data labeling for AI models is pushing research towards more efficient and robust weakly supervised learning methods.
This framework offers a principled approach to address a fundamental challenge in AI development, potentially making AI more accessible and scalable by reducing reliance on extensive human annotation.
Current fragmented, pattern-specific weakly supervised learning methods may be replaced by a unified, theoretically sound framework, improving stability and generality.
- · AI developers
- · SaaS companies
- · Data scientists
- · Companies with limited labeled data
- · Manual data labeling services
- · Companies relying on bespoke weak supervision techniques
Reduced costs and time for AI model development due to less reliance on fully labeled datasets.
Accelerated deployment of AI in domains where data labeling is prohibitively expensive or complex, broadening AI application.
The development of more autonomous AI systems that can learn effectively with minimal human supervision, akin to AI agents.
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Read at arXiv cs.LG