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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 1831–1840 of 2050 papers

TitleStatusHype
HybridSVD: When Collaborative Information is Not EnoughCode0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
Post-Selection Confidence Bounds for Prediction PerformanceCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
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