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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 221–230 of 2050 papers

TitleStatusHype
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
EPP: interpretable score of model predictive powerCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Differentiable Model Selection for Ensemble LearningCode0
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