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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 121–130 of 2050 papers

TitleStatusHype
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Modeling the Second Player in Distributionally Robust OptimizationCode1
Monitored Distillation for Positive Congruent Depth CompletionCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
BayesOpt Adversarial AttackCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
Automated Machine Learning in InsuranceCode1
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