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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 576–600 of 2050 papers

TitleStatusHype
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Bayesian Model Selection for Change Point Detection and Clustering—0
An Homotopy Algorithm for the Lasso with Online Observations—0
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—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
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Dirichlet Process Parsimonious Mixtures for clustering—0
Bayesian leave-one-out cross-validation for large data—0
Bayesian Learning with Wasserstein Barycenters—0
Bayesian Model Selection of Stochastic Block Models—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
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