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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 41–50 of 2050 papers

TitleStatusHype
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
An Asymptotic Equation Linking WAIC and WBIC in Singular Models—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
Zero-Shot Forecasting Mortality Rates: A Global Study—0
Exploring the Potential of SSL Models for Sound Event Detection—0
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