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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 76–100 of 2050 papers

TitleStatusHype
Learned harmonic mean estimation of the marginal likelihood with normalizing flowsCode1
Proximal nested sampling with data-driven priors for physical scientistsCode1
Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious FeaturesCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
LOVM: Language-Only Vision Model SelectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
On Pitfalls of Test-Time AdaptationCode1
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly DetectionCode1
PyVBMC: Efficient Bayesian inference in PythonCode1
Searching for Effective Neural Network Architectures for Heart Murmur Detection from PhonocardiogramCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Data thinning for convolution-closed distributionsCode1
Testing Firm ConductCode1
ExcelFormer: A neural network surpassing GBDTs on tabular dataCode1
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great BritainCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
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