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

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Showing 26–50 of 537 papers

TitleStatusHype
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction TaskCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
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
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
A Unified Approach to Interpreting Model PredictionsCode1
How Interpretable and Trustworthy are GAMs?Code1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Born-Again Tree EnsemblesCode1
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Benchmark Results

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