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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 51–75 of 2050 papers

TitleStatusHype
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
clusterBMA: Bayesian model averaging for clusteringCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Can We Characterize Tasks Without Labels or Features?Code1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
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