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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
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC—0
On U-processes and clustering performance—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
Face Recognition using Optimal Representation Ensemble—0
The LASSO risk: asymptotic results and real world examples—0
Discriminative Clustering by Regularized Information Maximization—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
Probabilistic latent variable models for distinguishing between cause and effect—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
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation—0
Sparsistent Learning of Varying-coefficient Models with Structural Changes—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Adaptive Design Optimization in Experiments with People—0
Data-driven calibration of linear estimators with minimal penalties—0
On the Reliability of Clustering Stability in the Large Sample Regime—0
Model selection and velocity estimation using novel priors for motion patterns—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Bolasso: model consistent Lasso estimation through the bootstrapCode0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Predictive Matrix-Variate t Models—0
Robust Regression with Twinned Gaussian Processes—0
On Sparsity and Overcompleteness in Image Models—0
A Bayesian Approach to Network Modularity—0
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