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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 1021–1030 of 2050 papers

TitleStatusHype
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
We Need to Talk About train-dev-test SplitsCode0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Simple data balancing achieves competitive worst-group-accuracyCode1
The Pareto Frontier of model selection for general Contextual Bandits—0
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in RCode1
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