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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 276–300 of 2050 papers

TitleStatusHype
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Fast Instrument Learning with Faster RatesCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Extremely Greedy Equivalence SearchCode0
Face Spoofing Detection using Deep LearningCode0
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-SpreadingCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Optimal design of experiments to identify latent behavioral typesCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
EPP: interpretable score of model predictive powerCode0
Differentiable Model Selection for Ensemble LearningCode0
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
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