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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 231–240 of 2050 papers

TitleStatusHype
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
A Unified Approach to Routing and Cascading for LLMs—0
A Unified Dynamic Approach to Sparse Model Selection—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
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