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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 1791–1800 of 2050 papers

TitleStatusHype
Optimal statistical decision for Gaussian graphical model selection—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Network cross-validation by edge sampling—0
Clipper: A Low-Latency Online Prediction Serving System—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
PAG2ADMG: An Algorithm for the Complete Causal Enumeration of a Markov Equivalence Class—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Statistical Inference for Pairwise Graphical Models Using Score Matching—0
Bayesian optimization for automated model selection—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
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