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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 876–900 of 2050 papers

TitleStatusHype
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Entropy-based Characterization of Modeling Constraints—0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
fETSmcs: Feature-based ETS model component selectionCode0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications—0
Diagnostic Tool for Out-of-Sample Model EvaluationCode0
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets—0
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models—0
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKCα, dynamicsCode0
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
On the safe use of prior densities for Bayesian model selection—0
Machine Learning Inference on Inequality of Opportunity—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
A Regret-Variance Trade-Off in Online Learning—0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
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