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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 251–260 of 2050 papers

TitleStatusHype
Towards Unsupervised Validation of Anomaly-Detection Models—0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
Impact of Missing Values in Machine Learning: A Comprehensive Analysis—0
Noether's razor: Learning Conserved QuantitiesCode1
Decision-Aware Predictive Model Selection for Workforce Allocation—0
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