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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
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptionsCode0
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous DatasetsCode0
Is it Fake? News Disinformation Detection on South African News WebsitesCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
Dynamic Model Tree for Interpretable Data Stream LearningCode0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataCode0
An exact counterfactual-example-based approach to tree-ensemble models interpretabilityCode0
Interpretable Machine Learning for Survival AnalysisCode0
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf ValuesCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Margin Optimal Classification TreesCode0
Relative Feature ImportanceCode0
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
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Interpretability with full complexity by constraining feature information—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Interpretability of machine learning based prediction models in healthcare—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Deducing neighborhoods of classes from a fitted model—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
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Benchmark Results

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