SOTAVerified

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 361–370 of 537 papers

TitleStatusHype
Motif-guided Time Series Counterfactual Explanations—0
Multi-Agent Algorithmic Recourse—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions—0
Multi-type Disentanglement without Adversarial Training—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Near Optimal Decision Trees in a SPLIT Second—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
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Benchmark Results

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