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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 121–130 of 537 papers

TitleStatusHype
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Is it Fake? News Disinformation Detection on South African News WebsitesCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
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
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Benchmark Results

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