Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, …
Additive modelsInterpretable Machine LearningSequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering
Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation …
ClusteringDiversityInterpretable Machine LearningAnalyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines
The COVID-19 vaccine development, manufacturing, transportation, and administration proved an extreme logistics operation of global magnitude. Global vaccination levels, however, remain a key concern in preventing the em…
Interpretable Machine LearningCultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevo…
DiversityInterpretable Machine LearningCategory-Specific Topological Learning of Metal-Organic Frameworks
Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and …
Interpretable Machine LearningProperty PredictionA comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The datase…
counterfactualEnsemble LearningInterpretable Machine LearningExplaining the Unexplained: Revealing Hidden Correlations for Better Interpretability
Deep learning has achieved remarkable success in processing and managing unstructured data. However, its "black box" nature imposes significant limitations, particularly in sensitive application domains. While existing i…
Feature CorrelationFeature Importanceimage-classificationImage Classification+4Data-driven model reconstruction for nonlinear wave dynamics
The use of machine learning to predict wave dynamics is a topic of growing interest, but commonly-used deep learning approaches suffer from a lack of interpretability of the trained models. Here we present an interpretab…
Interpretable Machine LearningmodelMCCE: Missingness-aware Causal Concept Explainer
Causal concept effect estimation is gaining increasing interest in the field of interpretable machine learning. This general approach explains the behaviors of machine learning models by estimating the causal effect of h…
Interpretable Machine LearningExpert Study on Interpretable Machine Learning Models with Missing Data
Inherently interpretable machine learning (IML) models provide valuable insights for clinical decision-making but face challenges when features have missing values. Classical solutions like imputation or excluding incomp…
Decision MakingImputationInterpretable Machine LearningMissing ValuesLearning Model Agnostic Explanations via Constraint Programming
Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. …
Interpretable Machine LearningmodelCross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation
Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical l…
Interpretable Machine LearningMulti-Label ClassificationMulti-Label Classification on ChestX-ray14Thorax Disease DetectionGraph Learning for Numeric Planning
Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrar…
Graph LearningInterpretable Machine LearningLearning local discrete features in explainable-by-design convolutional neural networks
Our proposed framework attempts to break the trade-off between performance and explainability by introducing an explainable-by-design convolutional neural network (CNN) based on the lateral inhibition mechanism. The Expl…
Explainable Artificial Intelligence (XAI)Image ClassificationInterpretable Machine LearningInfo-CELS: Informative Saliency Map Guided Counterfactual Explanation
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for build…
counterfactualCounterfactual ExplanationExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+1Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions
We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and …
Experimental DesignInterpretable Machine LearningSpecificityEstablishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning
Power outages have become increasingly frequent, intense, and prolonged in the US due to climate change, aging electrical grids, and rising energy demand. However, largely due to the absence of granular spatiotemporal ou…
Interpretable Machine LearningKernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
Banzhaf values offer a simple and interpretable alternative to the widely-used Shapley values. We introduce Kernel Banzhaf, a novel algorithm inspired by KernelSHAP, that leverages an elegant connection between Banzhaf v…
Interpretable Machine LearningGAMformer: In-Context Learning for Generalized Additive Models
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such…
Additive modelsIn-Context LearningInterpretable Machine Learning"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their compressed world knowledge to generate int…
Interpretable Machine LearningWorld Knowledge