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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 471–480 of 2050 papers

TitleStatusHype
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Convex Techniques for Model Selection—0
Selective machine learning of doubly robust functionals—0
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