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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 921–930 of 2050 papers

TitleStatusHype
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
Statistical Model Criticism of Variational Auto-Encoders—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Fundamental limits to learning closed-form mathematical models from data—0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
System Identification via Nuclear Norm RegularizationCode0
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