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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 226–250 of 2050 papers

TitleStatusHype
Exploring Model Transferability through the Lens of Potential EnergyCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Automated Model Selection for Tabular DataCode0
Optimal design of experiments to identify latent behavioral typesCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
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