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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 851–860 of 2050 papers

TitleStatusHype
A Strong Baseline for Batch Imitation Learning—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Graphical LASSO Based Model Selection for Time Series—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Graph Similarity Description: How Are These Graphs Similar?—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Bayesian Boosting for Linear Mixed Models—0
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