arXiv:2503.18509v2 Announce Type: replace Abstract: Weak supervision enables machine learning models to learn from limited or noisy labels, but it introduces challenges in reliability and semantic clarity, particularly in multi-instance partial label learning (MI-PLL), where models must resolve both ambiguous supervision signals and uncertain instance-label mappings. This paper proposes a semantics for a neuro-symbolic framework that integrates inductive logic programming (ILP) to structure MI-PLL through relational constraints. In this formulation, ILP defines a hypothesis space over label tr
The increasing complexity and unreliability of machine learning models relying on weak supervision necessitate new theoretical frameworks to enhance their robustness and interpretability.
Improving the reliability and semantic clarity of weakly supervised AI models is crucial for their deployment in high-stakes applications and for advancing the overall capabilities of AI systems.
This research provides a theoretical foundation for integrating symbolic reasoning with neural networks in weakly supervised learning, potentially leading to more explainable and trustworthy AI.
- · AI developers
- · Machine learning researchers
- · Industries adopting complex AI systems
- · Developers of opaque black-box AI models
- · Systems highly reliant on purely statistical weak supervision
AI models will become more capable of learning from ambiguous data while maintaining a higher degree of interpretability due to relational constraints.
The integration of neuro-symbolic AI in weak supervision could accelerate the development of more robust AI agents for complex tasks.
Improved AI reliability and interpretability could foster greater public trust and reduce regulatory friction in AI adoption across sensitive sectors.
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Read at arXiv cs.AI