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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 1101–1110 of 2050 papers

TitleStatusHype
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
We Need to Talk About train-dev-test SplitsCode0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
The Pareto Frontier of model selection for general Contextual Bandits—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems—0
On Model Selection Consistency of Lasso for High-Dimensional Ising Models—0
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