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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 751–760 of 2050 papers

TitleStatusHype
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks—0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
Testing Firm ConductCode1
Understanding Best Subset Selection: A Tale of Two C(omplex)ities—0
ExcelFormer: A neural network surpassing GBDTs on tabular dataCode1
Guided Recommendation for Model Fine-Tuning—0
BiasBed - Rigorous Texture Bias EvaluationCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Bayesian Interpolation with Deep Linear Networks—0
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