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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 1821–1830 of 2050 papers

TitleStatusHype
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Multiple Testing and Variable Selection along the path of the Least Angle RegressionCode0
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Multiple testing for signal-agnostic searches of new physics with machine learningCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
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