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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 151–175 of 2050 papers

TitleStatusHype
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
An Information-theoretic Approach to Distribution ShiftsCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DriveML: An R Package for Driverless Machine LearningCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Automating Outlier Detection via Meta-LearningCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
How Many Topics? Stability Analysis for Topic ModelsCode1
Automated Machine Learning in InsuranceCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
In Search of Lost Domain GeneralizationCode1
BERTScore: Evaluating Text Generation with BERTCode1
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