Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations

GNN Explainability in a regression setting - semester project for Applied Mathematics MSc @ EPFL

Repository for the paper 'CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models'.

Explain model and feature dependencies by decomposition of SHAP values

Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.

Interpretable text embeddings by asking LLMs yes/no questions

For calculating global feature importance using Shapley values.

Influence Estimation for Gradient-Boosted Decision Trees

ES-HyperNEAT Python implementation with C++ computations for NeuroEvolution, Reinforcement Learning and VfMRI

GraphXAI: Resource to support the development and evaluation of GNN explainers

Concise summaries of key papers in responsible AI.

🗺️ Data Cleaning and Textual Data Visualization 🗺️

Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023

Local Universal Rule-based Explanations

[ICML'24] Official PyTorch Implementation of TimeX++

AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.

Post-host prototype-based explanations with rules for time-series classifiers

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper "Learning to Receive Help: Intervention-Aware Concept Embedding Models"

Evaluating XAI methods through ablation studies.

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.