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A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
Simplicial Homology Global Optimization
This repository was created as an implementation approach for a project on "Watermarking Deep Neural Networks".
Black-box Few-shot Knowledge Distillation
📄 [Talk] OFFZONE 2022 / ODS Data Halloween 2022: Black-box attacks on ML models + with use of open-source tools
Tiny Tutorial on https://arxiv.org/abs/1703.04730
Generative Adversarial Network (GAN) that can produce tabular samples given datasets, and build a general generative model that receives a black-box as a discriminator and can still generate samples from the tabular data.
Getting explanations for predictions made by black box models.
This repository contains the function "fitss" for state-space model of black-box Voltage Source Converters for application in small-signal stability analysis in modern power-systems.
Using LIME and SHAP for model interpretability of Machine Learning Black-box models.
Interpreting Categorical Data Classifiers using Explanation-based Locality
Research toolkit for reverse-engineering neural networks in strict black-box settings
This repository presents a comprehensive research paper exploring the role of Explainable Artificial Intelligence (XAI) in modern Machine Learning. It aims to shed light on the interpretability of 'black-box' models like Neural Networks, Explainable AI and highlights the need for transparent, human-understandable ML systems.
Evaluating model inversion attacks and defenses with differential privacy and sub-sampling in deep learning models
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