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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 401–425 of 2050 papers

TitleStatusHype
Exploring Design Choices for Building Language-Specific LLMsCode0
LaPLACE: Probabilistic Local Model-Agnostic Causal ExplanationsCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Face Spoofing Detection using Deep LearningCode0
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously ThoughtCode0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKCα, dynamicsCode0
Learning Equations for Extrapolation and ControlCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre studyCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Best Arm Identification for Stochastic Rising BanditsCode0
Model selection for contextual banditsCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text RankingCode0
GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnaceCode0
Increasing certainty in systems biology models using Bayesian multimodel inferenceCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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