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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 1161–1170 of 2050 papers

TitleStatusHype
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation—0
Last Layer Marginal Likelihood for Invariance LearningCode0
Model Selection for Bayesian AutoencodersCode0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
Time Series Anomaly Detection with label-free Model Selection—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
Feature and Parameter Selection in Stochastic Linear Bandits—0
Bayesian Boosting for Linear Mixed Models—0
On the Use of Minimum Penalties in Statistical Learning—0
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