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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 351–400 of 537 papers

TitleStatusHype
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
The Partial Response Network: a neural network nomogram—0
Model Bridging: Connection between Simulation Model and Neural Network—0
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
Motif-guided Time Series Counterfactual Explanations—0
Multi-Agent Algorithmic Recourse—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions—0
Multi-type Disentanglement without Adversarial Training—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Near Optimal Decision Trees in a SPLIT Second—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—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
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
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