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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 791–800 of 2050 papers

TitleStatusHype
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Structured model selection via _1-_2 optimizationCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
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