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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 1671–1680 of 2050 papers

TitleStatusHype
Binary Matrix Factorization via Dictionary Learning—0
A comparison of methods for model selection when estimating individual treatment effectsCode1
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Large-Scale Model Selection with Misspecification—0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI—0
Piecewise Convex Function Estimation and Model Selection—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
HybridSVD: When Collaborative Information is Not EnoughCode0
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