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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 401–450 of 537 papers

TitleStatusHype
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Overcoming Catastrophic Forgetting by XAI—0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Toward More Generalized Malicious URL Detection Models—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Pest presence prediction using interpretable machine learning—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia—0
Towards Analogy-Based Explanations in Machine Learning—0
An interpretable machine learning approach for ferroalloys consumptions—0
Towards A Rigorous Science of Interpretable Machine Learning—0
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
Predictive learning via rule ensembles—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Towards making NLG a voice for interpretable Machine Learning—0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems—0
A Learning Theoretic Perspective on Local Explainability—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
Reducing Optimism Bias in Incomplete Cooperative Games—0
Variable Selection via Thompson Sampling—0
Diagnostic-free onboard battery health assessment—0
Differentiable Genetic Programming for High-dimensional Symbolic Regression—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
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Benchmark Results

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