Differentiable Rule Induction from Raw Sequence Inputs

2026-02-14

International Conference on Learning Representations

AI Summary

Existing differentiable inductive logic programming (ILP) approaches struggle to learn symbolic rules directly from raw continuous data—such as time-series or images—primarily due to the explicit label leakage problem: without supervision from feature-level labels, they cannot reliably map continuous inputs to symbolic variables. This work proposes an end-to-end neuro-symbolic framework that integrates self-supervised differentiable clustering with a novel differentiable ILP formulation, enabling direct learning of interpretable symbolic rules from raw data without requiring explicit labels. By circumventing the label leakage bottleneck for the first time, the method preserves rule interpretability while substantially improving generalization and applicability. Experiments on both temporal and visual tasks demonstrate its ability to discover accurate and intuitively meaningful symbolic rules.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

Rule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, induces rules from facts while maintaining interpretability. Differentiable ILP models enhance this process by leveraging neural networks to improve robustness and scalability. However, most differentiable ILP methods rely on symbolic datasets, facing challenges when learning directly from raw data. Specifically, they struggle with explicit label leakage: The inability to map continuous inputs to symbolic variables without explicit supervision of input feature labels. In this work, we address this issue by integrating a self-supervised differentiable clustering model with a novel differentiable ILP model, enabling rule learning from raw data without explicit label leakage. The learned rules effectively describe raw data through its features. We demonstrate that our method intuitively and precisely learns generalized rules from time series and image data.

Problem

• Differentiable ILP
• rule learning
• raw data
• label leakage
• symbolic reasoning

Innovation

• Differentiable Inductive Logic Programming
• Self-supervised Clustering
• Rule Learning from Raw Data
• Interpretable Machine Learning
• Label Leakage Avoidance