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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 201–250 of 537 papers

TitleStatusHype
Explanations for Automatic Speech Recognition—0
Structural Neural Additive Models: Enhanced Interpretable Machine LearningCode1
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
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
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
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—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
Mixture of Decision Trees for Interpretable Machine LearningCode1
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
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
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
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Margin Optimal Classification TreesCode0
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
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devicesCode0
TSInterpret: A unified framework for time series interpretabilityCode1
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
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Benchmark Results

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