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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 351–360 of 2050 papers

TitleStatusHype
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Blocked Clusterwise Regression—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
Block-diagonal covariance selection for high-dimensional Gaussian graphical models—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
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