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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 751–775 of 2050 papers

TitleStatusHype
DGSAC: Density Guided Sampling and Consensus—0
Face Recognition using Optimal Representation Ensemble—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Factor-Augmented Regularized Model for Hazard Regression—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Bayesian Hierarchical Community Discovery—0
Factors in Fashion: Factor Analysis towards the Mode—0
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
High SNR Consistent Compressive Sensing—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
Fast and fully-automated histograms for large-scale data sets—0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research—0
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Fast Linear Model Trees by PILOT—0
Fast model selection by limiting SVM training times—0
Bayesian Evidence and Model Selection—0
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