SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2041–2050 of 2050 papers

TitleStatusHype
Making Tree Ensembles Interpretable: A Bayesian Model Selection ApproachCode0
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical ModelCode0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Statistical Inference for Sequential Feature Selection after Domain AdaptationCode0
Transformers for Green Semantic Communication: Less Energy, More SemanticsCode0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
CHARDA: Causal Hybrid Automata Recovery via Dynamic AnalysisCode0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Show:102550
← PrevPage 205 of 205Next →

No leaderboard results yet.