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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 701–725 of 2050 papers

TitleStatusHype
SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems—0
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning—0
Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection—0
Conjugate Mixture Models for Clustering Multimodal Data—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Conformal Prediction with Upper and Lower Bound Models—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Compressive Recovery of Signals Defined on Perturbed Graphs—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Local Information Criterion for Dynamical Systems—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
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