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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 1141–1150 of 2050 papers

TitleStatusHype
The Future of Employment Revisited: How Model Selection Determines Automation Forecasts—0
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT RankersCode0
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity—0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
Where and What? Examining Interpretable Disentangled RepresentationsCode1
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
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