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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 101–125 of 2050 papers

TitleStatusHype
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Hologram Reasoning for Solving Algebra Problems with Geometry DiagramsCode1
BayesOpt Adversarial AttackCode1
BERTScore: Evaluating Text Generation with BERTCode1
Data thinning for convolution-closed distributionsCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
In Search of Lost Domain GeneralizationCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Deep Domain Confusion: Maximizing for Domain InvarianceCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
An information criterion for automatic gradient tree boostingCode1
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