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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 51–100 of 2050 papers

TitleStatusHype
A stacked DCNN to predict the RUL of a turbofan engineCode1
Extended Stochastic Block Models with Application to Criminal NetworksCode1
Forecasting with time series imagingCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Hologram Reasoning for Solving Algebra Problems with Geometry DiagramsCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
DriveML: An R Package for Driverless Machine LearningCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
BERTScore: Evaluating Text Generation with BERTCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great BritainCode1
Deep learning for dynamic graphs: models and benchmarksCode1
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
Estimating Generalization under Distribution Shifts via Domain-Invariant RepresentationsCode1
An Information-theoretic Approach to Distribution ShiftsCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Data thinning for convolution-closed distributionsCode1
Deep Domain Confusion: Maximizing for Domain InvarianceCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
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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