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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 451–475 of 2050 papers

TitleStatusHype
Asymptotic Model Selection for Directed Networks with Hidden Variables—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—0
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
A Local Information Criterion for Dynamical Systems—0
Selective machine learning of doubly robust functionals—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Conformal Prediction with Upper and Lower Bound Models—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Conjugate Mixture Models for Clustering Multimodal Data—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem—0
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
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