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Found 5 papers, 1 papers with code
Closure operators: Complexity and applications to classification and decision-making
We study the complexity of closure operators, with applications to machine learning and decision theory.
Aggregation of Pareto optimal models
Under these four steps, we show that all rational/consistent aggregation rules are as follows: Give each individual Pareto optimal model a weight, introduce a weak order/ranking over the set of Pareto optimal models, aggregate a finite set of models S as the model associated with the prior obtained as the weighted average of the priors of the highest-ranked models in S. This result shows that all rational/consistent aggregation rules must follow a generalization of hierarchical Bayesian modeling.
Aggregation of Models, Choices, Beliefs, and Preferences
In this paper, we show that all rational aggregation rules are of the form (3).
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball
Although (C) leads to the identification of an optimal prior, its approximation suffers from the curse of dimensionality and the notion of risk is one that is averaged with respect to the distribution of the data.
Decision Theoretic Bootstrapping
The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution.
