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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 991–1000 of 2050 papers

TitleStatusHype
Causal Discovery in Hawkes Processes by Minimum Description Length—0
On the safe use of prior densities for Bayesian model selection—0
Machine Learning Inference on Inequality of Opportunity—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
A Regret-Variance Trade-Off in Online Learning—0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
Understanding new tasks through the lens of training data via exponential tiltingCode0
Verifying Learning-Based Robotic Navigation Systems—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
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