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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 421–430 of 537 papers

TitleStatusHype
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predictive learning via rule ensembles—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Towards making NLG a voice for interpretable Machine Learning—0
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Benchmark Results

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