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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 231–240 of 2050 papers

TitleStatusHype
Evaluating LLP Methods: Challenges and ApproachesCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
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