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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 126–150 of 537 papers

TitleStatusHype
Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource SettingsCode0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf ValuesCode0
Learning local discrete features in explainable-by-design convolutional neural networksCode0
Modelling wildland fire burn severity in California using a spatial Super Learner approachCode0
Interpretable Machine Learning for Survival AnalysisCode0
Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case StudyCode0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning ModelsCode0
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningCode0
Interpretable Explanations of Black Boxes by Meaningful PerturbationCode0
Challenging common interpretability assumptions in feature attribution explanationsCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing FlowsCode0
From Human Explanation to Model Interpretability: A Framework Based on Weight of EvidenceCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
iNNvestigate neural networks!Code0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous DatasetsCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified