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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 661–670 of 2050 papers

TitleStatusHype
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Structured model selection via _1-_2 optimizationCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
Green Runner: A tool for efficient model selection from model repositories—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
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