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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 1601–1610 of 2050 papers

TitleStatusHype
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
On the Use of Minimum Penalties in Statistical Learning—0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring—0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law—0
On uncertainty-penalized Bayesian information criterion—0
On U-processes and clustering performance—0
OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?—0
Open, Closed, or Small Language Models for Text Classification?—0
Open Problem: Model Selection for Contextual Bandits—0
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
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