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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 676–700 of 2050 papers

TitleStatusHype
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
GPT in Data Science: A Practical Exploration of Model Selection—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
Supervised structure learning—0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages—0
Comparison of model selection techniques for seafloor scattering statistics—0
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count DataCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Unsupervised Video Summarization via Iterative Training and Simplified GANCode0
Saturn: Efficient Multi-Large-Model Deep Learning—0
Changing the Kernel During Training Leads to Double Descent in Kernel RegressionCode0
An energy-based comparative analysis of common approaches to text classification in the Legal domain—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
NoMoPy: Noise Modeling in Python—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Evaluating LLP Methods: Challenges and ApproachesCode0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
Causal Q-Aggregation for CATE Model Selection—0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
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