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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 251–300 of 537 papers

TitleStatusHype
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance—0
Novel Topological Shapes of Model Interpretability—0
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models—0
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency—0
On Explaining Decision Trees—0
On Interpretability and Similarity in Concept-Based Machine Learning—0
Online Product Feature Recommendations with Interpretable Machine Learning—0
On quantitative aspects of model interpretability—0
On the definition and importance of interpretability in scientific machine learning—0
Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default—0
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach—0
On the Shape of Brainscores for Large Language Models (LLMs)—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Overcoming Catastrophic Forgetting by XAI—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Pest presence prediction using interpretable machine learning—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predictive learning via rule ensembles—0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)—0
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower—0
Rethinking Interpretability in the Era of Large Language Models—0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data—0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures—0
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Benchmark Results

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