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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 1626–1650 of 2050 papers

TitleStatusHype
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Use Of Vapnik-Chervonenkis Dimension in Model Selection—0
Robust high dimensional factor models with applications to statistical machine learning—0
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation—0
Cross Validation Based Model Selection via Generalized Method of Moments—0
Is it worth it? Budget-related evaluation metrics for model selection—0
Tune: A Research Platform for Distributed Model Selection and TrainingCode0
Optimal design of experiments to identify latent behavioral typesCode0
Automatic Gradient BoostingCode0
Pairwise Covariates-adjusted Block Model for Community Detection—0
Algebraic Equivalence of Linear Structural Equation ModelsCode0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Probabilistic Boolean Tensor DecompositionCode0
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP ModelsCode0
Learning Equations for Extrapolation and ControlCode0
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical ModelCode0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
Stationary Geometric Graphical Model Selection—0
Degrees of Freedom and Model Selection for k-means ClusteringCode0
Agreement-based Learning—0
Structural Learning of Multivariate Regression Chain Graphs via Decomposition—0
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