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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 1981–1990 of 2050 papers

TitleStatusHype
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary OutputsCode0
Learning the mechanisms of network growthCode0
The Shape of Learning Curves: a ReviewCode0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
On the Computational Complexity of Private High-dimensional Model SelectionCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
The temporal overfitting problem with applications in wind power curve modelingCode0
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