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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 2026–2050 of 2050 papers

TitleStatusHype
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
Sparse Estimation with Structured Dictionaries—0
Face Recognition using Optimal Representation Ensemble—0
Discriminative Clustering by Regularized Information Maximization—0
Probabilistic latent variable models for distinguishing between cause and effect—0
The LASSO risk: asymptotic results and real world examples—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
Classification with Scattering Operators—0
Regularization for Cox's proportional hazards model with NP-dimensionality—0
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical ModelsCode0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation—0
Adaptive Design Optimization in Experiments with People—0
Sparsistent Learning of Varying-coefficient Models with Structural Changes—0
Data-driven calibration of linear estimators with minimal penalties—0
On the Reliability of Clustering Stability in the Large Sample Regime—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Model selection and velocity estimation using novel priors for motion patterns—0
Bolasso: model consistent Lasso estimation through the bootstrapCode0
On Sparsity and Overcompleteness in Image Models—0
Predictive Matrix-Variate t Models—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Robust Regression with Twinned Gaussian Processes—0
A Bayesian Approach to Network Modularity—0
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