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 151–200 of 537 papers

TitleStatusHype
Interpretability of machine learning based prediction models in healthcare—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—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
Insights into the origin of halo mass profiles from machine learning—0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Causal rule ensemble approach for multi-arm data—0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
Causal Entropy and Information Gain for Measuring Causal Control—0
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
Causal Dependence Plots—0
Explaining Kernel Clustering via Decision Trees—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Explanations for Automatic Speech Recognition—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Extract Local Inference Chains of Deep Neural Nets—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Full interpretable machine learning in 2D with inline coordinates—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
GAMformer: In-Context Learning for Generalized Additive Models—0
GAM(L)A: An econometric model for interpretable Machine Learning—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Explainable AI using expressive Boolean formulas—0
Show:102550
← PrevPage 4 of 11Next →

Benchmark Results

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