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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 1451–1475 of 2050 papers

TitleStatusHype
Adaptive debiased machine learning using data-driven model selection techniques—0
Adaptive Design Optimization in Experiments with People—0
Adaptive LASSO estimation for functional hidden dynamic geostatistical model—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Adaptive Online Learning—0
Adaptive Sequential Machine Learning—0
Adaptive variational Bayes: Optimality, computation and applications—0
A data-centric approach to class-specific bias in image data augmentation—0
A deep learning based solution for construction equipment detection: from development to deployment—0
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
Agreement-based Learning—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
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