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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 1211–1220 of 2050 papers

TitleStatusHype
On Statistical Efficiency in LearningCode0
Leave Zero Out: Towards a No-Cross-Validation Approach for Model SelectionCode0
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL—0
Flexible, Non-parametric Modeling Using Regularized Neural NetworksCode0
Speech Enhancement with Zero-Shot Model SelectionCode0
Odd-One-Out Representation LearningCode0
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation—0
Smooth Bandit Optimization: Generalization to Hölder Space—0
Comparison of Anomaly Detectors: Context MattersCode0
Conjugate Mixture Models for Clustering Multimodal Data—0
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