Abstract
Weak supervision allows machine learning models to learn from limited or noisy labels, but it introduces challenges in interpretability and reliability - particularly in multi-instance partial label learning (MI-PLL), where models must resolve both ambiguous labels and uncertain instance-label mappings. We propose a semantics for neuro-symbolic framework that integrates Inductive Logic Programming (ILP) to improve MI-PLL by providing structured relational constraints that guide learning. Within our semantic characterization, ILP defines a logical hypothesis space for label transitions, clarifies classifier semantics, and establishes interpretable performance standards. This hybrid approach improves robustness, transparency, and accountability in weakly supervised settings, ensuring neural predictions align with domain knowledge. By embedding weak supervision into a logical framework, we enhance both interpretability and learning, making weak supervision more suitable for real-world, high-stakes applications.
Abstract (translated)
弱监督允许机器学习模型在有限或有噪声的标签下进行学习，但这也带来了可解释性和可靠性方面的挑战——尤其是在多实例部分标记学习（MI-PLL）中，模型必须解决模棱两可的标签和不确定的实例-标签映射问题。我们提出了一种神经符号框架的语义表达方式，该方式结合了归纳逻辑编程（ILP），以通过提供结构化的关系约束来指导学习从而改进 MI-PLL。在我们的语义描述中，ILP 定义了一个逻辑假设空间用于标签转换，明确了分类器的意义，并确立了可解释性的性能标准。这种混合方法提高了弱监督环境下的稳健性、透明度和问责制，确保神经预测与领域知识相一致。通过将弱监督嵌入到逻辑框架中，我们增强了其可解释性和学习能力，使弱监督更适合于现实世界中的高风险应用。
URL
https://arxiv.org/abs/2503.18509
