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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 426–450 of 537 papers

TitleStatusHype
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