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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 376–400 of 537 papers

TitleStatusHype
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review—0
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance—0
Novel Topological Shapes of Model Interpretability—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—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
A hybrid machine learning framework for analyzing human decision making through learning preferences—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
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—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
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—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
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Topological data analysis of zebrafish patterns—0
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Benchmark Results

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