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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 251–260 of 2050 papers

TitleStatusHype
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
Bayesian Network Models for Adaptive Testing—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Local Information Criterion for Dynamical Systems—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
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