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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 26–50 of 2050 papers

TitleStatusHype
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU TasksCode2
Optimizing Model Selection for Compound AI SystemsCode2
A network approach to topic modelsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
Can We Characterize Tasks Without Labels or Features?Code1
An information criterion for automatic gradient tree boostingCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
BERTScore: Evaluating Text Generation with BERTCode1
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