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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 76–100 of 2050 papers

TitleStatusHype
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
An information criterion for automatic gradient tree boostingCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Assumption-lean inference for generalised linear model parametersCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
A network approach to topic modelsCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Automated Machine Learning in InsuranceCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Automating Outlier Detection via Meta-LearningCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
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