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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 601–625 of 2050 papers

TitleStatusHype
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo—0
Defining Expertise: Applications to Treatment Effect EstimationCode0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media—0
Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data—0
Learning under Singularity: An Information Criterion improving WBIC and sBIC—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Online Foundation Model Selection in Robotics—0
Model Assessment and Selection under Temporal Distribution ShiftCode0
Compressive Recovery of Signals Defined on Perturbed Graphs—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Understanding Model Selection For Learning In Strategic Environments—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Label-Efficient Model Selection for Text Generation—0
Selective linear segmentation for detecting relevant parameter changes—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
Best Practices for Text Annotation with Large Language Models—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
Surfing the modeling of PoS taggers in low-resource scenarios—0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
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