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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 1061–1070 of 2050 papers

TitleStatusHype
Estimation of Local Average Treatment Effect by Data Combination—0
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits—0
Learning the hypotheses space from data through a U-curve algorithm—0
Adaptive variational Bayes: Optimality, computation and applications—0
Functional additive models on manifolds of planar shapes and forms—0
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT—0
Optimization Networks for Integrated Machine Learning—0
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones—0
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
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