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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 1101–1110 of 2050 papers

TitleStatusHype
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
Feature and Parameter Selection in Stochastic Linear Bandits—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
On the Use of Minimum Penalties in Statistical Learning—0
Bayesian Boosting for Linear Mixed Models—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
An Information-theoretic Approach to Distribution ShiftsCode1
AI without networks—0
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