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 121–130 of 537 papers

TitleStatusHype
Challenges in Variable Importance Ranking Under Correlation—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
Rethinking Interpretability in the Era of Large Language Models—0
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic RegressionCode0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
X Hacking: The Threat of Misguided AutoMLCode0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource SettingsCode0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
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Benchmark Results

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