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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 1871–1880 of 2050 papers

TitleStatusHype
Energy-Aware Dynamic Neural Inference—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Enhancing Certifiable Robustness via a Deep Model Ensemble—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Ensemble Reinforcement Learning: A Survey—0
Entropy-based Characterization of Modeling Constraints—0
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19—0
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