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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 591–600 of 2050 papers

TitleStatusHype
Parameter identifiability and model selection for partial differential equation models of cell invasionCode0
Generalized Information Criteria for Structured Sparse Models—0
AutoML-GPT: Large Language Model for AutoML—0
Saturn: An Optimized Data System for Large Model Deep Learning WorkloadsCode1
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks—0
Subjectivity in Unsupervised Machine Learning Model Selection—0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Sparse Models for Machine Learning—0
Non-Bayesian Post-Model-Selection Estimation as Estimation Under Model Misspecification—0
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