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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 451–475 of 2050 papers

TitleStatusHype
Execution-based Evaluation for Data Science Code Generation ModelsCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
On the Evaluation of Conditional GANsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Context tree selection for functional dataCode0
EPP: interpretable score of model predictive powerCode0
AALF: Almost Always Linear ForecastingCode0
Bayesian Neural Networks at Finite TemperatureCode0
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