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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 537 papers

TitleStatusHype
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
Explanations for Automatic Speech Recognition—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Additive Higher-Order Factorization Machines—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Challenges in Variable Importance Ranking Under Correlation—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Causal rule ensemble approach for multi-arm data—0
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Benchmark Results

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