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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 537 papers

TitleStatusHype
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island CoevolutionCode0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Data-driven model reconstruction for nonlinear wave dynamics—0
MCCE: Missingness-aware Causal Concept Explainer—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
Learning Model Agnostic Explanations via Constraint Programming—0
Learning local discrete features in explainable-by-design convolutional neural networksCode0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf ValuesCode0
GAMformer: In-Context Learning for Generalized Additive Models—0
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning ModelsCode0
Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapyCode0
LLM-based feature generation from text for interpretable machine learningCode0
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Benchmark Results

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