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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 1831–1840 of 2050 papers

TitleStatusHype
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Dominant Drivers of National Inflation—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
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
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