JonathanCrabbe / Label-Free-XAI
This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
☆23Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for Label-Free-XAI
- An Empirical Framework for Domain Generalization In Clinical Settings☆27Updated 2 years ago
- Code for the paper "Model Agnostic Interpretability for Multiple Instance Learning".☆13Updated 2 years ago
- Code to study the generalisability of benchmark models on non-stationary EHRs.☆14Updated 5 years ago
- This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help …☆23Updated last year
- Resources for Machine Learning Explainability☆68Updated 2 months ago
- Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper…☆52Updated this week
- EMNLP'22 | PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning☆26Updated last year
- Codebase for information theoretic shapley values to explain predictive uncertainty.This repo contains the code related to the paperWatso…☆18Updated 4 months ago
- Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"☆18Updated 3 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆52Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- ☆41Updated last year
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 2 years ago
- ☆12Updated 2 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆36Updated 6 months ago
- [ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions …☆24Updated 9 months ago
- Improving the Fairness of Chest X-ray Classifiers☆14Updated 2 years ago
- An Empirical Study of Invariant Risk Minimization☆28Updated 4 years ago
- Code for the ICLR 2022 paper "Attention-based interpretability with Concept Transformers"☆39Updated last year
- Official implementation of ICLR 2020 paper Unsupervised Clustering using Pseudo-semi-supervised Learning☆48Updated 3 years ago
- Neural Additive Models (Google Research)☆26Updated 6 months ago
- A pytorch implemention of the Explainable AI work 'Contrastive layerwise relevance propagation (CLRP)'☆17Updated 2 years ago
- A benchmark for distribution shift in tabular data☆44Updated 5 months ago
- h-Shap provides an exact, fast, hierarchical implementation of Shapley coefficients for image explanations☆15Updated last year
- Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift☆35Updated last year
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆73Updated 2 years ago
- This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human con…☆9Updated 2 years ago
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated 6 months ago
- ☆25Updated 6 months ago
- ☆26Updated last year