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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 301–325 of 537 papers

TitleStatusHype
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Designing Inherently Interpretable Machine Learning ModelsCode2
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon SetCode0
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Shapley variable importance clouds for interpretable machine learningCode1
Multi-Agent Algorithmic Recourse—0
Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients—0
Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Is it Fake? News Disinformation Detection on South African News WebsitesCode0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
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Benchmark Results

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