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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 591–600 of 2050 papers

TitleStatusHype
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Pre-Trained Model Recommendation for Downstream Fine-tuning—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits—0
Regularized DeepIV with Model Selection—0
A data-centric approach to class-specific bias in image data augmentation—0
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
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