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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 1801–1825 of 2050 papers

TitleStatusHype
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Detection and Evaluation of Clusters within Sequential Data—0
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Determination of Latent Dimensionality in International Trade Flow—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
DGSAC: Density Guided Sampling and Consensus—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Differentially Private Generalized Linear Models Revisited—0
Differentially Private Learning with Margin Guarantees—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
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