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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 611–620 of 2050 papers

TitleStatusHype
Model Assessment and Selection under Temporal Distribution ShiftCode0
Understanding Model Selection For Learning In Strategic Environments—0
Compressive Recovery of Signals Defined on Perturbed Graphs—0
Label-Efficient Model Selection for Text Generation—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Selective linear segmentation for detecting relevant parameter changes—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
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