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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 181–190 of 537 papers

TitleStatusHype
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
On the Shape of Brainscores for Large Language Models (LLMs)—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Online Learning of Decision Trees with Thompson SamplingCode0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Interpretable Machine Learning for Weather and Climate Prediction: A Survey—0
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Benchmark Results

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