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 226–250 of 537 papers

TitleStatusHype
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Insights into the origin of halo mass profiles from machine learning—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Interpretability of machine learning based prediction models in healthcare—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Brain Age from the Electroencephalogram of Sleep—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
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Benchmark Results

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