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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 221–230 of 2050 papers

TitleStatusHype
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Mitigating covariate shift in non-colocated data with learned parameter priors—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration—0
Model Selection for Average Reward RL with Application to Utility Maximization in Repeated Games—0
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
GeMID: Generalizable Models for IoT Device IdentificationCode0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
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