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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 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
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
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Benchmark Results

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