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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 351–375 of 537 papers

TitleStatusHype
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
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
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
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behaviorCode0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataCode0
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Learning Gradual Argumentation Frameworks using Genetic AlgorithmsCode0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Developing a Fidelity Evaluation Approach for Interpretable Machine LearningCode0
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU PatientsCode0
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Benchmark Results

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