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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 1741–1750 of 2050 papers

TitleStatusHype
Bolasso: model consistent Lasso estimation through the bootstrapCode0
Towards Accurate Model Selection in Deep Unsupervised Domain AdaptationCode0
Gaussian Process Subspace Regression for Model ReductionCode0
GeMID: Generalizable Models for IoT Device IdentificationCode0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza DataCode0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution DiscriminationCode0
Symmetric Linear Bandits with Hidden SymmetryCode0
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