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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 631–640 of 2050 papers

TitleStatusHype
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Dynamic Model Selection for Prediction Under a Budget—0
Bayesian leave-one-out cross-validation for large data—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Eagle: Efficient Training-Free Router for Multi-LLM Inference—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
Bayesian Learning with Wasserstein Barycenters—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
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