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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 531–540 of 2050 papers

TitleStatusHype
Evaluation of HTR models without Ground Truth MaterialCode0
Batch Value-function Approximation with Only RealizabilityCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video AnalyticsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
EPP: interpretable score of model predictive powerCode0
Differentiable Model Selection for Ensemble LearningCode0
Adaptive Mixtures of Factor AnalyzersCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
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