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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 601–610 of 2050 papers

TitleStatusHype
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Disentangling Factors of Variations Using Few Labels—0
Disentangling Factors of Variation Using Few Labels—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Distributed filtered hyperinterpolation for noisy data on the sphere—0
Bayesian leave-one-out cross-validation for large data—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Bayesian Learning with Wasserstein Barycenters—0
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