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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 81–90 of 2050 papers

TitleStatusHype
A comparison of methods for model selection when estimating individual treatment effectsCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
An information criterion for automatic gradient tree boostingCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Deep learning for dynamic graphs: models and benchmarksCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
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