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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 881–890 of 2050 papers

TitleStatusHype
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
A Survey on Theoretical Advances of Community Detection in Networks—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Comparing Bayesian Models of Annotation—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
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