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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 276–300 of 537 papers

TitleStatusHype
Shapley variable importance cloud for machine learning models—0
MAntRA: A framework for model agnostic reliability analysis—0
Fast Parallel Exact Inference on Bayesian Networks: PosterCode0
Interpretability with full complexity by constraining feature information—0
Overcoming Catastrophic Forgetting by XAI—0
A Generic Approach for Reproducible Model DistillationCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine LearningCode0
Motif-guided Time Series Counterfactual Explanations—0
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach—0
Margin Optimal Classification TreesCode0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Conditional Feature Importance for Mixed DataCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
Understanding Interventional TreeSHAP : How and Why it WorksCode0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
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Benchmark Results

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