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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 631–640 of 2050 papers

TitleStatusHype
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Differentiable Model Selection for Ensemble LearningCode0
Automatic Gradient BoostingCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
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