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Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Comprehensive suite for rule-based learning
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
XCSF learning classifier system: rule-based online evolutionary machine learning
The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020
Implementation of Anticipatory Learning Classifiers System (ALCS) in Python
PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization
A Java implementation for LORD, a rule learning algorithm proposed in the article "Efficient learning of large sets of locally optimal classification rules" with the approach of searching for a locally optimal rule for each training example. Machine Learning, volume 112, pages 571–610 (2023)
Explain fully connected ReLU neural networks using rules
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules
Learn rule-based models from examples using LLM-powered synthesis. Replace expensive LLM calls with fast, deterministic, inspectable regex, code, or spaCy rules.
End-to-end differentiable rule learning for fraud detection. A neural network discovers its own IF-THEN rules via temperature annealing — no hand-coding required.
Documentation of the BOOMER machine learning algorithm.
Implementation of pruning hypothesis space using domain theories -- M. Svatoš, G. Šourek, F. Zeležný, S. Schockaert, and O. Kuželka: Pruning Hypothesis Spaces Using Learned Domain Theories, ILP'17
Research codebase for the project "When rule learning breaks: Diffusion Fails to Learn Parity of Many Bits"
Implementation of P3C-tree (Partial Pre-Post Code Tree) published in KDD2025. P3C-tree is significantly saves memory compared with PPC-tree, by factors of some to some tens and runs faster while generating the same N-lists for a given input data.
A rule learning algorithm for the deduction of syndrome definitions from time series data.
Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20
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