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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 671–680 of 2050 papers

TitleStatusHype
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
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