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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 441–450 of 2050 papers

TitleStatusHype
Towards Unsupervised Validation of Anomaly-Detection Models—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
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
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
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
Decision-Aware Predictive Model Selection for Workforce Allocation—0
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
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