feature-attribution

Model interpretability and understanding for PyTorch

Shapley Interactions and Shapley Values for Machine Learning

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Collection of NLP model explanations and accompanying analysis tools

An Open-Source Library for the interpretability of time series classifiers

Explainable AI in Julia.

A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.

Counterfactual SHAP: a framework for counterfactual feature importance

Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑

Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍

The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"

Code and data for the ACL 2023 NLReasoning Workshop paper "Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods" (Feldhus et al., 2023)

⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"

Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.

Efficient and accurate explanation estimation with distribution compression (ICLR 2025 Spotlight)

Reproducible code for our paper "Explainable Learning with Gaussian Processes"

Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)

Codes for the paper On marginal feature attributions of tree-based models

Robustness of Global Feature Effect Explanations (ECML PKDD 2024)

NO2 Prediction: Performance and Robustness Comparison between Random Forest and Graph Neural Network