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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 951–975 of 2050 papers

TitleStatusHype
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Inferring Network Structure From Data—0
Convex Techniques for Model Selection—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Bayesian Boosting for Linear Mixed Models—0
Joint Continuous and Discrete Model Selection via Submodularity—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Information criteria for non-normalized models—0
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
Correcting Model Bias with Sparse Implicit Processes—0
Bayesian Anomaly Detection and Classification—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Interpretability in Linear Brain Decoding—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
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