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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 251–300 of 537 papers

TitleStatusHype
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
Differentiable Genetic Programming for High-dimensional Symbolic Regression—0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing FlowsCode0
Verifying Properties of Tsetlin MachinesCode0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Causal Dependence Plots—0
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning modelCode0
Knowledge Discovery from Atomic Structures using Feature Importances—0
Explanations for Automatic Speech Recognition—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Shapley variable importance cloud for machine learning models—0
MAntRA: A framework for model agnostic reliability analysis—0
Fast Parallel Exact Inference on Bayesian Networks: PosterCode0
Interpretability with full complexity by constraining feature information—0
Overcoming Catastrophic Forgetting by XAI—0
A Generic Approach for Reproducible Model DistillationCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine LearningCode0
Motif-guided Time Series Counterfactual Explanations—0
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach—0
Margin Optimal Classification TreesCode0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Conditional Feature Importance for Mixed DataCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
Understanding Interventional TreeSHAP : How and Why it WorksCode0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
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Benchmark Results

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