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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 651–675 of 2050 papers

TitleStatusHype
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
LOVM: Language-Only Vision Model SelectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
On Pitfalls of Test-Time AdaptationCode1
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Structured model selection via _1-_2 optimizationCode0
Green Runner: A tool for efficient model selection from model repositories—0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Rethinking the Evaluation Protocol of Domain GeneralizationCode1
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Automatic Model Selection with Large Language Models for ReasoningCode1
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Boldness-Recalibration for Binary Event Predictions—0
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