moDel Agnostic Language for Exploration and eXplanation
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Updated
Jan 20, 2026 - Python
moDel Agnostic Language for Exploration and eXplanation
An automatic obfuscation tool for Android apps that works in a black-box fashion, supports advanced obfuscation features and has a modular architecture easily extensible with new techniques
A Python module for parallel optimization of expensive black-box functions
Yet another black-box optimization library for Python
code for our CVPR 2022 paper "DINE: Domain Adaptation from Single and Multiple Black-box Predictors"
Simplicial Homology Global Optimization
Automated black-box REST API testing using graph-based modeling, LLMs, and multi-agent reinforcement learning.
Causal Responsibility EXplanations for Image Classifiers and Tabular Data
Simple grammar-based test case generator
[EMNLP'24] MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning
[NeurIPS'20] Learning Black-Box Attackers with Transferable Priors and Query Feedback
Design and Cryptanalysis of Symmetric-Key Algorithms in Black and White-box Models
Adaptive stress testing of black-box systems within POMDPs.jl
Black-box Few-shot Knowledge Distillation
Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.
A set of reactor design benchmark problems to evaluate high-dimensional, expensive, and potentially multi-fidelity optimisation algorithms.
Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks
The code for paper 'Membership Inference on LLMs in the Wild'
Implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation (Fong, et. al., 2018)
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