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Found 3 papers, 2 papers with code
Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features
We propose the approach of tuning the hyperparameters of a predictive model in a multi-criteria fashion with respect to predictive accuracy and feature selection stability.
Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features
For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost identical but have different identifiers as different features.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper.
