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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 1421–1430 of 2050 papers

TitleStatusHype
The Reciprocal Bayesian LASSOCode0
Marked point processes and intensity ratios for limit order book modeling—0
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection—0
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
On hyperparameter tuning in general clustering problemsm—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
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