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
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Subgroup Analysis via Model-based Rule Forest—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
META-ANOVA: Screening interactions for interpretable machine learning—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
A Survey of Malware Detection Using Deep Learning—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning PredictionsCode0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Tensor Polynomial Additive Model—0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*Code0
Learning Discrete Concepts in Latent Hierarchical Models—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear RegressionCode0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
On the Shape of Brainscores for Large Language Models (LLMs)—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Online Learning of Decision Trees with Thompson SamplingCode0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Interpretable Machine Learning for Weather and Climate Prediction: A Survey—0
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
Leveraging advances in machine learning for the robust classification and interpretation of networks—0
Interpretable Machine Learning for Survival AnalysisCode0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine LearningCode0
LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models—0
Explaining Kernel Clustering via Decision Trees—0
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
Challenges in Variable Importance Ranking Under Correlation—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
Show:102550
← PrevPage 4 of 11Next →

Benchmark Results

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