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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 1461–1470 of 2050 papers

TitleStatusHype
A Dirichlet stochastic block model for composition-weighted networks—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding—0
Adversarial Negotiation Dynamics in Generative Language Models—0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A first econometric analysis of the CRIX family—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
Aggregation of Affine Estimators—0
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