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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 301–325 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
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Benchmark Results

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