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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 481–490 of 2050 papers

TitleStatusHype
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Differentiable Model Selection for Ensemble LearningCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
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