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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 101–125 of 2050 papers

TitleStatusHype
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
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
BayesOpt Adversarial AttackCode1
Fuzzy c-Means Clustering for Persistence DiagramsCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
How Many Topics? Stability Analysis for Topic ModelsCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Automating Outlier Detection via Meta-LearningCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
In Search of Lost Domain GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
LCE: An Augmented Combination of Bagging and Boosting in PythonCode1
An Information-theoretic Approach to Distribution ShiftsCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
mikropml: User-Friendly R Package for Supervised Machine Learning PipelinesCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
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