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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 1121–1130 of 2050 papers

TitleStatusHype
Robust and Parallel Bayesian Model Selection—0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
Robust Graphical Modeling with t-Distributions—0
Robust high dimensional factor models with applications to statistical machine learning—0
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models—0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations—0
Robust Model Selection of Gaussian Graphical Models—0
Robustness in sparse linear models: relative efficiency based on robust approximate message passing—0
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