SOTAVerified

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

Showing 2650 of 537 papers

TitleStatusHype
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitisCode1
Structural Neural Additive Models: Enhanced Interpretable Machine LearningCode1
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
Mixture of Decision Trees for Interpretable Machine LearningCode1
TSInterpret: A unified framework for time series interpretabilityCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Neural Basis Models for InterpretabilityCode1
Scalable Interpretability via PolynomialsCode1
Towards Better Understanding Attribution MethodsCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
POTATO: exPlainable infOrmation exTrAcTion framewOrkCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Shapley variable importance clouds for interpretable machine learningCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Trees with Attention for Set Prediction TasksCode1
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Benchmark Results

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