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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 2026–2050 of 2050 papers

TitleStatusHype
Cross-Validation with ConfidenceCode0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
Cross-Validated Off-Policy EvaluationCode0
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical ModelsCode0
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Risk Controlled Image RetrievalCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer VisionCode0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
Batch Value-function Approximation with Only RealizabilityCode0
Machine learning in policy evaluation: new tools for causal inferenceCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimizationCode0
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
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