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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 611–620 of 2050 papers

TitleStatusHype
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
A Review of Change of Variable Formulas for Generative Modeling—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
Predictive Modeling through Hyper-Bayesian Optimization—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading—0
Learning Disentangled Discrete RepresentationsCode0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Anytime Model Selection in Linear BanditsCode0
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