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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
Carbon Intensity-Aware Adaptive Inference of DNNs—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Inferring bias and uncertainty in camera calibration—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
Inferring Network Structure From Data—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A Bayesian Approach to Network Modularity—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—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
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
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Interpretability in Linear Brain Decoding—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
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