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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 1811–1820 of 2050 papers

TitleStatusHype
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Differentially Private Generalized Linear Models Revisited—0
Differentially Private Learning with Margin Guarantees—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
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