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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
Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Thompson Sampling-like Algorithms for Stochastic Rising Bandits—0
Zero-Shot Forecasting Mortality Rates: A Global Study—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
A Systematic Analysis of Base Model Choice for Reward Modeling—0
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