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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 1151–1160 of 2050 papers

TitleStatusHype
SC-Safety: A Multi-round Open-ended Question Adversarial Safety Benchmark for Large Language Models in Chinese—0
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases—0
Second-Order Convergence in Private Stochastic Non-Convex Optimization—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Cost-based feature selection for network model choice—0
Selective Factor Extraction in High Dimensions—0
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