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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 61–70 of 537 papers

TitleStatusHype
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction TaskCode1
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals PredictionCode1
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit LayersCode1
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
How Interpretable and Trustworthy are GAMs?Code1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
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Benchmark Results

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