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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 426–450 of 2050 papers

TitleStatusHype
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Fast Instrument Learning with Faster RatesCode0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Minimum discrepancy principle strategy for choosing k in k-NN regressionCode0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
Model Assessment and Selection under Temporal Distribution ShiftCode0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global WarmingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Model Selection for Bayesian AutoencodersCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Model Selection in Bayesian Neural Networks via Horseshoe PriorsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Morphological Segmentation for SenecaCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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