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 311–320 of 537 papers

TitleStatusHype
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Learning Model Agnostic Explanations via Constraint Programming—0
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
A Survey of Malware Detection Using Deep Learning—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Subgroup Analysis via Model-based Rule Forest—0
A Semiparametric Approach to Interpretable Machine Learning—0
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
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Benchmark Results

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