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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
Extended Stochastic Block Models with Application to Criminal NetworksCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
DriveML: An R Package for Driverless Machine LearningCode1
BayesOpt Adversarial AttackCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
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
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Data thinning for convolution-closed distributionsCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
An Information-theoretic Approach to Distribution ShiftsCode1
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
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
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
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
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
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
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
Automated Machine Learning in InsuranceCode1
BERTScore: Evaluating Text Generation with BERTCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
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