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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 701–710 of 2050 papers

TitleStatusHype
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
PyVBMC: Efficient Bayesian inference in PythonCode1
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Solar Power Prediction Using Machine Learning—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
A variational synthesis of evolutionary and developmental dynamics—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
Searching for Effective Neural Network Architectures for Heart Murmur Detection from PhonocardiogramCode1
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