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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 451–460 of 2050 papers

TitleStatusHype
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
A Local Information Criterion for Dynamical Systems—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
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