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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 421–430 of 2050 papers

TitleStatusHype
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Conformal online model aggregationCode0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
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