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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 221–230 of 537 papers

TitleStatusHype
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
A Decision-Theoretic Approach for Model Interpretability in Bayesian FrameworkCode0
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
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Benchmark Results

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