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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 1401–1425 of 2050 papers

TitleStatusHype
Dataless Model Selection with the Deep Frame Potential—0
Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality—0
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems—0
Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance—0
A new approach in model selection for ordinal target variables—0
Model Selection in Contextual Stochastic Bandit Problems—0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
Determination of Latent Dimensionality in International Trade Flow—0
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs—0
Learning Gaussian Graphical Models via Multiplicative Weights—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Learning the Hypotheses Space from data Part II: Convergence and Feasibility—0
Blocked Clusterwise Regression—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
The Reciprocal Bayesian LASSOCode0
Marked point processes and intensity ratios for limit order book modeling—0
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection—0
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
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