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 501–537 of 537 papers

TitleStatusHype
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change—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
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization—0
GAMformer: In-Context Learning for Generalized Additive Models—0
GAM(L)A: An econometric model for interpretable Machine Learning—0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—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
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Comparing interpretability and explainability for feature selection—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Hidden Citations Obscure True Impact in Science—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
Challenges in Variable Importance Ranking Under Correlation—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
Causal rule ensemble approach for multi-arm data—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning—0
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
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Benchmark Results

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