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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 571–580 of 2050 papers

Title	Date	Tasks	Status	Score
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation	Oct 21, 2021	Model SelectionTime Series	CodeCode Available	5
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting	Dec 20, 2023	AutoMLMeta-Learning	CodeCode Available	5
Differentiable Model Selection for Ensemble Learning	Nov 1, 2022	Combinatorial OptimizationEnsemble Learning	CodeCode Available	5
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance	Jan 29, 2025	DiversityMMLU	CodeCode Available	5
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes	Feb 17, 2022	Decision MakingInterpretable Machine Learning	CodeCode Available	5
Deep Generalized Method of Moments for Instrumental Variable Analysis	May 29, 2019	Model Selection	CodeCode Available	5
Automated Dependence Plots	Dec 2, 2019	Bias DetectionModel Selection	CodeCode Available	5
Diagnostic Tool for Out-of-Sample Model Evaluation	Jun 22, 2022	Diagnosticmodel	CodeCode Available	5
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning	Jan 22, 2018	Model SelectionNode Classification	CodeCode Available	5
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference	Feb 21, 2022	Common Sense ReasoningModel Selection	CodeCode Available	5

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