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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 1–25 of 537 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
SmoothGrad: removing noise by adding noiseCode4
Learning Important Features Through Propagating Activation DifferencesCode4
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
Designing Inherently Interpretable Machine Learning ModelsCode2
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
OmniXAI: A Library for Explainable AICode2
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Axiomatic Attribution for Deep NetworksCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
A Unified Approach to Interpreting Model PredictionsCode1
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Benchmark Results

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