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interpretable-ml
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Fit interpretable models. Explain blackbox machine learning.
Model interpretability and understanding for PyTorch
A curated list of awesome responsible machine learning resources.
High-Performance Symbolic Regression in Python and Julia
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
Distributed High-Performance Symbolic Regression in Julia
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI 
)
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
An Open-Source Library for the interpretability of time series classifiers
Explainable Machine Learning in Survival Analysis
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Optimal Sparse Decision Trees
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
A list of research papers of explainable machine learning.
A PyTorch implementation of constrained optimization and modeling techniques