Ecosyste.ms: Awesome
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awesome-efficient-xai
https://github.com/ynchuang/awesome-efficient-xai
- Interpretable machine learning
- Techniques for Interpretable Machine Learning
- Explainable Recommendation: A Survey and New Perspectives
- Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
- Accelerating Shapley Explanation via Contributive cooperator Selection
- Groupshapley: Efficient Prediction Explanation with Shapley Values for Feature Group
- Polynomial Calculation of the Shapley Value based on Sampling
- Antithetic and Monte Carlo Kernel Estimators for Partial Rankings
- Sampling Permutations for Shapley Value Estimation
- Scaling Guarantees for Nearest Counterfactual Explanations
- Optimal Counterfactual Explanations in Tree Ensembles
- Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
- Efficient Search for Diverse Coherent Explanations
- Model-Agnostic Counterfactual Explanations of Recommendations
- DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
- Multi-Objective Counterfactual Explanations
- Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers
- Influence-Driven Explanations for Bayesian Network Classiers
- Interpretable Counterfactual Explanations Guided by Prototypes
- Counterfactual Shapley Additive Explanations
- Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
- Efficient Computation and Analysis of Distributional Shapley Values
- Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
- Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf
- Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
- L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
- FastSHAP: Real-Time Shapley Value Estimation
- CoRTX: Contrastive Framework for Real-time Explanation
- Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
- CXPlain: Causal Explanations for Model Interpretation under Uncertainty
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods
- Shapley Explanation Networks
- Learning to Explain with Complemental Examples
- Attention-like feature explanation for tabular data
- Efficient Explanations from Empirical Explainers
- Fast Axiomatic Attribution for Neural Networks
- Model-Based Counterfactual Synthesizer for Interpretation
- Explanation by Progressive Exaggeration
- Explaining the Black-box Smoothly- A Counterfactual Approach
- Getting a CLUE: A Method for Explaining Uncertainty Estimates
- Cycle-Consistent Counterfactuals by Latent Transformations
- Learning Model-Agnostic Counterfactual Explanations for Tabular Data
- Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
- Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
- VCNet: A Self-explaining Model for Realistic Counterfactual Generation
- CRUDS: Counterfactual Recourse Using Disentangled Subspaces
- Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
- xGEMs: Generating Examplars to Explain Black-Box Models
- Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
- CLEAR: Generative Counterfactual Explanations on Graphs
- ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
- Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models
- Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
- Detecting Statistical Interactions from Neural Network Weights
- Towards Interaction Detection Using Topological Analysis on Neural Networks
- Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
- Faith-Shap: The Faithful Shapley Interaction Index
- The Shapley Taylor Interaction Index
- XDeep: an Open-source Python Library for Interpretable Machine Learning
- SHAP: A a Game Theoretic Package to Explain Machine Learning Models
- Captum: Model Interpretability for PyTorch
- OmniXAI: A Library for Explainable AI
- AIX 360: An Open-source Toolkit of Explainable Machine Learning
- InterpretML: A Toolkit to Help Understand Models and Enable Responsible Machine Learning
- Alibi Explain: An Open-source Python library for Machine Learning Model Inspection and Interpretation
- OpenXAI: Towards a Transparent Evaluation of Model Explanations
- Awesome Interpretable Machine Learning
- Awesome Machine Learning Interpretability
- Awesome Fairness in AI
- A Guide for Making Black Box Models Explainable
- A Chinese Open Course of Interpretable Machine Learning
Programming Languages
Keywords
machine-learning
6
interpretability
5
xai
4
interpretable-machine-learning
3
explainable-ai
3
deep-learning
2
explainable-ml
2
explainability
2
data-science
2
fairness
2
interpretable-ai
2
interpretable-ml
2
explanation
1
counterfactual
1
explanations
1
benchmark
1
leaderboard
1
reproducibility
1
shapley
1
shap
1
gradient-boosting
1
gender-bias
1
bias
1
ai
1
transparency
1
secure-ml
1
reliable-ai
1
r
1
python
1
privacy-preserving-machine-learning
1
privacy-enhancing-technologies
1
machine-learning-interpretability
1
awesome-list
1
awesome
1
ai-safety
1