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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 301–350 of 537 papers

TitleStatusHype
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devicesCode0
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
An Additive Instance-Wise Approach to Multi-class Model InterpretationCode0
Linguistically inspired roadmap for building biologically reliable protein language models—0
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous DatasetsCode0
Additive Higher-Order Factorization Machines—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Pest presence prediction using interpretable machine learning—0
Efficient Learning of Interpretable Classification Rules—0
SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contextsCode0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Insights into the origin of halo mass profiles from machine learning—0
Local Explanation of Dimensionality ReductionCode0
An interpretable machine learning approach for ferroalloys consumptions—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data—0
Dynamic Model Tree for Interpretable Data Stream LearningCode0
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems—0
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
GAM(L)A: An econometric model for interpretable Machine Learning—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Interpretable machine learning in Physics—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
Toward More Generalized Malicious URL Detection ModelsCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Interpreting a Machine Learning Model for Detecting Gravitational Waves—0
REPID: Regional Effect Plots with implicit Interaction DetectionCode0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
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Benchmark Results

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