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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 1231–1240 of 2050 papers

TitleStatusHype
Pareto Optimal Model Selection in Linear Bandits—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Derivative-Free Reinforcement Learning: A Review—0
Ising Model Selection Using _1-Regularized Linear Regression: A Statistical Mechanics Analysis—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Vine copula mixture models and clustering for non-Gaussian dataCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
Sequential prediction under log-loss and misspecification—0
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