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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 731–740 of 2050 papers

TitleStatusHype
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
A Convex Framework for Confounding Robust InferenceCode0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
The Topology and Geometry of Neural RepresentationsCode0
Error Reduction from Stacked Regressions—0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
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