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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 1971–1980 of 2050 papers

TitleStatusHype
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Sparse Inverse Covariance Estimation with Calibration—0
Bayesian Hierarchical Community Discovery—0
Learning Adaptive Value of Information for Structured Prediction—0
On model selection consistency of penalized M-estimators: a geometric theory—0
Score-based Causal Learning in Additive Noise Models—0
Exact post-selection inference, with application to the lasso—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Aggregation of Affine Estimators—0
Parsimonious Shifted Asymmetric Laplace Mixtures—0
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