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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 51–100 of 537 papers

TitleStatusHype
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Data-driven model reconstruction for nonlinear wave dynamics—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
MCCE: Missingness-aware Causal Concept Explainer—0
Learning Model Agnostic Explanations via Constraint Programming—0
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Learning local discrete features in explainable-by-design convolutional neural networksCode0
Graph Learning for Numeric PlanningCode1
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf ValuesCode0
GAMformer: In-Context Learning for Generalized Additive Models—0
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning ModelsCode0
Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapyCode0
LLM-based feature generation from text for interpretable machine learningCode0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Subgroup Analysis via Model-based Rule Forest—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
META-ANOVA: Screening interactions for interpretable machine learning—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
A Survey of Malware Detection Using Deep Learning—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning PredictionsCode0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Tensor Polynomial Additive Model—0
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*Code0
Learning Discrete Concepts in Latent Hierarchical Models—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear RegressionCode0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
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Benchmark Results

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