Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
☆82Dec 8, 2022Updated 3 years ago
Alternatives and similar repositories for Fooling-LIME-SHAP
Users that are interested in Fooling-LIME-SHAP are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Local explanations with uncertainty 💐!☆42Aug 8, 2023Updated 2 years ago
- bayesian lime☆18Aug 3, 2024Updated last year
- Supervised Local Modeling for Interpretability☆29Oct 27, 2018Updated 7 years ago
- [ICLR 2022] Boosting Randomized Smoothing with Variance Reduced Classifiers☆11Mar 29, 2022Updated 3 years ago
- A Diagnostic Study of Explainability Techniques for Text Classification☆70Oct 23, 2020Updated 5 years ago
- S-LIME: Stabilized-LIME for model explanation (KDD 2021). pip install stabilized-lime☆16Mar 7, 2026Updated 2 weeks ago
- Explaining Image Classifiers by Counterfactual Generation☆28Apr 23, 2022Updated 3 years ago
- A Unified Approach to Evaluate and Compare Explainable AI methods☆14Jan 19, 2024Updated 2 years ago
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆29Jul 13, 2019Updated 6 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆45Aug 1, 2021Updated 4 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Feb 23, 2022Updated 4 years ago
- For calculating global feature importance using Shapley values.☆285Mar 16, 2026Updated last week
- Source code for the Joint Shapley values: a measure of joint feature importance☆12Sep 14, 2021Updated 4 years ago
- This repository provides a PyTorch implementation of "Fooling Neural Network Interpretations via Adversarial Model Manipulation". Our pap…☆23Dec 19, 2020Updated 5 years ago
- ☆14Jun 22, 2020Updated 5 years ago
- This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Lea…☆15Aug 17, 2021Updated 4 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆77Mar 26, 2022Updated 3 years ago
- ☆14Apr 23, 2019Updated 6 years ago
- Deep Weighted Averaging Classifiers☆23Feb 4, 2019Updated 7 years ago
- CCS 2023 | Explainable malware and vulnerability detection with XAI in paper "FINER: Enhancing State-of-the-art Classifiers with Feature …☆11Aug 20, 2024Updated last year
- Explanation Optimization☆13Oct 16, 2020Updated 5 years ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16May 9, 2019Updated 6 years ago
- ☆16May 9, 2022Updated 3 years ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆252Aug 17, 2024Updated last year
- ☆51Aug 29, 2020Updated 5 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆25Sep 4, 2022Updated 3 years ago
- An amortized approach for calculating local Shapley value explanations☆104Nov 29, 2023Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆39Jun 21, 2022Updated 3 years ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,501Jul 13, 2025Updated 8 months ago
- PyTorch implementation of SmoothTaylor☆15Sep 5, 2021Updated 4 years ago
- Layer-wise Relevance Propagation (LRP) for LSTMs.☆225Apr 24, 2020Updated 5 years ago
- ☆10Mar 13, 2023Updated 3 years ago
- Code and documentation for experiments in the TreeExplainer paper☆190Oct 4, 2019Updated 6 years ago
- ☆14Dec 4, 2023Updated 2 years ago
- Papers and code of Explainable AI esp. w.r.t. Image classificiation☆226Jun 28, 2022Updated 3 years ago
- Tools for robustness evaluation in interpretability methods☆10Jun 25, 2021Updated 4 years ago
- ☆19Mar 24, 2021Updated 5 years ago
- Data Shapley: Equitable Valuation of Data for Machine Learning☆290May 1, 2024Updated last year
- A Novel Hybrid Deep Learning Model for Heterogeneous Tabular Data☆22Jan 4, 2025Updated last year