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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 651–660 of 2050 papers

TitleStatusHype
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs—0
Data-driven calibration of linear estimators with minimal penalties—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation—0
Crowd-SFT: Crowdsourcing for LLM Alignment—0
Cross Validation Based Model Selection via Generalized Method of Moments—0
Automated Model Selection with Bayesian Quadrature—0
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