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 421–430 of 537 papers

TitleStatusHype
Quantifying and Learning Linear Symmetry-Based DisentanglementCode0
A Learning Theoretic Perspective on Local Explainability—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
On Explaining Decision Trees—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsCode0
Novel Topological Shapes of Model Interpretability—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
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Benchmark Results

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