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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 2001–2050 of 2050 papers

TitleStatusHype
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
Mixture Model Averaging for Clustering—0
Active Comparison of Prediction Models—0
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Weighted Likelihood Policy Search with Model Selection—0
Deep Gaussian Processes—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Online Learning with Predictable Sequences—0
Model Selection for Degree-corrected Block Models—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Cross-Validation via Sequential TestingCode0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Multi-objective Exploratory Procedure for Regression Model Selection—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Scikit-learn: Machine Learning in PythonCode0
Large Scale Correlation Clustering OptimizationCode0
Sparse Estimation with Structured Dictionaries—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
Greedy Model Averaging—0
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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