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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
Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsCode0
Novel Topological Shapes of Model Interpretability—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
Quantifying and Learning Disentangled Representations with Limited Supervision—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
Deducing neighborhoods of classes from a fitted model—0
Socio-economic disparities and COVID-19 in the USACode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Relative Feature ImportanceCode0
On quantitative aspects of model interpretability—0
Variable Selection via Thompson Sampling—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
A Semiparametric Approach to Interpretable Machine Learning—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
Towards Analogy-Based Explanations in Machine Learning—0
Interpreting Neural Ranking Models using Grad-CAM—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
Offensive Language Detection ExplainedCode0
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin LiquidsCode0
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Benchmark Results

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