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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 251–300 of 537 papers

TitleStatusHype
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
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
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
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
OmniXAI: A Library for Explainable AICode2
Additive Higher-Order Factorization Machines—0
Neural Basis Models for InterpretabilityCode1
Scalable Interpretability via PolynomialsCode1
Towards Better Understanding Attribution MethodsCode1
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
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Local Explanation of Dimensionality ReductionCode0
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
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
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
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
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
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
POTATO: exPlainable infOrmation exTrAcTion framewOrkCode1
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
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Benchmark Results

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