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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 301–310 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
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Benchmark Results

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