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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 1811–1820 of 2050 papers

TitleStatusHype
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Multi-locus data distinguishes between population growth and multiple merger coalescentsCode0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning ActivationsCode0
Multimodal Benchmarking and Recommendation of Text-to-Image Generation ModelsCode0
Distributionally Robust Formulation and Model Selection for the Graphical LassoCode0
SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree ModelsCode0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
Probabilistic Modeling for Sequences of Sets in Continuous-TimeCode0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
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