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

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Showing 276–300 of 537 papers

TitleStatusHype
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 Models—0
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