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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 611–620 of 2050 papers

TitleStatusHype
Bayesian leave-one-out cross-validation for large data—0
Bayesian Learning with Wasserstein Barycenters—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Dominant Drivers of National Inflation—0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection—0
Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
Doubly robust off-policy evaluation with shrinkage—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
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