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 411–420 of 537 papers

TitleStatusHype
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationCode1
Relative Feature ImportanceCode0
On quantitative aspects of model interpretability—0
Variable Selection via Thompson Sampling—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
Generalized and Scalable Optimal Sparse Decision TreesCode1
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Benchmark Results

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