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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 171–180 of 2050 papers

TitleStatusHype
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
Assumption-lean inference for generalised linear model parametersCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
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