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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

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Showing 251–275 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
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Benchmark Results

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