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 3 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
- Code for "Generative causal explanations of black-box classifiers"☆35Updated 4 years ago
- Concept Bottleneck Models, ICML 2020☆215Updated 2 years ago
- Resources for Machine Learning Explainability☆86Updated last year
- Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper…☆70Updated this week
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆53Updated 3 years ago
- A repository for summaries of recent explainable AI/Interpretable ML approaches☆84Updated last year
- The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era comp…☆98Updated 2 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆54Updated 3 years ago
- Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023☆84Updated last year
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆61Updated last year
- An amortized approach for calculating local Shapley value explanations☆100Updated last year
- DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks☆21Updated 3 months ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆84Updated 2 years ago
- Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotligh…☆44Updated last year
- 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
- For calculating Shapley values via linear regression.☆71Updated 4 years ago
- [ICLR 23] A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled c…☆113Updated last year
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆248Updated last year
- [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift☆108Updated 2 years ago
- NeurIPS 2021 | Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information☆34Updated 3 years ago
- Improving the Fairness of Chest X-ray Classifiers☆14Updated 3 years ago
- A benchmark for distribution shift in tabular data☆55Updated last year
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆75Updated 3 years ago
- Self-Explaining Neural Networks☆43Updated 5 years ago
- ☆18Updated 5 years ago
- Papers and code of Explainable AI esp. w.r.t. Image classificiation☆218Updated 3 years ago
- Local explanations with uncertainty 💐!☆40Updated 2 years ago
- ☆18Updated 2 years ago