XMLX GitHub configuration
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Updated
Nov 23, 2021
XMLX GitHub configuration
INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
Default Risk Prediction from bank dataset with Interpretable Machine Learning
Fairness in AI and Machine Learning
Demonstration of InterpretME, an interpretable machine learning pipeline
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
Overview of machine learning interpretation techniques and their implementations
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
An interpretable machine learning pipeline over knowledge graphs
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Rule Extraction from Bayesian Networks
Article for Special Edition of Information: Machine Learning with Python
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
H2O.ai Machine Learning Interpretability Resources
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
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