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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 341–350 of 537 papers

TitleStatusHype
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry—0
MCCE: Missingness-aware Causal Concept Explainer—0
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
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Benchmark Results

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