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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 326–350 of 537 papers

TitleStatusHype
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans—0
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
Additive Higher-Order Factorization Machines—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
MAntRA: A framework for model agnostic reliability analysis—0
The Doctor Just Won't Accept That!—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry—0
MCCE: Missingness-aware Causal Concept Explainer—0
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
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Benchmark Results

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