JonathanCrabbe / Label-Free-XAILinks
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
Alternatives and similar repositories for Label-Free-XAI
Users that are interested in Label-Free-XAI are comparing it to the libraries listed below
Sorting:
- An Empirical Framework for Domain Generalization In Clinical Settings☆30Updated 3 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆55Updated 2 years ago
- Code to study the generalisability of benchmark models on non-stationary EHRs.☆14Updated 5 years ago
- Improving the Fairness of Chest X-ray Classifiers☆14Updated 3 years ago
- Resources for Machine Learning Explainability☆80Updated 9 months ago
- A benchmark for distribution shift in tabular data☆53Updated last year
- Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper…☆63Updated last month
- Code for the paper "Model Agnostic Interpretability for Multiple Instance Learning".☆13Updated 3 years ago
- Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"☆18Updated 4 years ago
- ☆12Updated 2 years ago
- This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help …☆24Updated 2 years ago
- Code for "Consistent Estimators for Learning to Defer to an Expert" (ICML 2020)☆13Updated 2 years ago
- ☆18Updated 5 years ago
- Supercharging Imbalanced Data Learning WithCausal Representation Transfer☆12Updated 3 years ago
- A pytorch implemention of the Explainable AI work 'Contrastive layerwise relevance propagation (CLRP)'☆17Updated 3 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆74Updated 3 years ago
- ☆46Updated 4 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆34Updated last year
- Codebase for information theoretic shapley values to explain predictive uncertainty.This repo contains the code related to the paperWatso…☆21Updated 11 months ago
- The code for the paper "MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement"☆20Updated last year
- Self-Explaining Neural Networks☆42Updated 5 years ago
- Code for "Generative causal explanations of black-box classifiers"☆34Updated 4 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆53Updated 3 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated last year
- ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition☆37Updated 2 years ago
- Source code for a comprehensive analysis of MTL over EHR timeseries data.☆37Updated 2 years ago
- GitHub repository for KDD 2021 work: ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classificati…☆12Updated 4 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- LISA for ICML 2022☆49Updated 2 years ago