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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 1776–1800 of 2050 papers

TitleStatusHype
The variational Laplace approach to approximate Bayesian inference—0
Modeling non-stationarities in high-frequency financial time series—0
Objective Bayesian Analysis for Change Point Problems—0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Parameter Selection Algorithm For Continuous Variables—0
Online Learning with Regularized Kernel for One-class Classification—0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Optimal statistical decision for Gaussian graphical model selection—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Network cross-validation by edge sampling—0
Clipper: A Low-Latency Online Prediction Serving System—0
Bayesian optimization for automated model selection—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Statistical Inference for Pairwise Graphical Models Using Score Matching—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
PAG2ADMG: An Algorithm for the Complete Causal Enumeration of a Markov Equivalence Class—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary OutputsCode0
Analyzing Framing through the Casts of Characters in the News—0
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity—0
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria—0
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