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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 401–410 of 537 papers

TitleStatusHype
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction TaskCode1
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
Deducing neighborhoods of classes from a fitted model—0
Socio-economic disparities and COVID-19 in the USACode0
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals PredictionCode1
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit LayersCode1
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
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Benchmark Results

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