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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 21–30 of 2050 papers

TitleStatusHype
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Machine-learning Growth at Risk—0
pared: Model selection using multi-objective optimizationCode0
DeSocial: Blockchain-based Decentralized Social NetworksCode1
Weighted Leave-One-Out Cross Validation—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models—0
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