5 papers
Closure operators: Complexity and applications to classification and decision-making
Hamed Hamze Bajgiran, Federico Echenique
We study the complexity of closure operators, with applications to machine learning and decision theory. In machine learning, closure operators emerge naturally in data classificat…
Aggregation of Pareto optimal models
Hamed Hamze Bajgiran, Houman Owhadi
In statistical decision theory, a model is said to be Pareto optimal (or admissible) if no other model carries less risk for at least one state of nature while presenting no more r…
Aggregation of Models, Choices, Beliefs, and Preferences
Hamed Hamze Bajgiran, Houman Owhadi
A natural notion of rationality/consistency for aggregating models is that, for all (possibly aggregated) models and , if the output of model is and if the output…
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball
Hamed Hamze Bajgiran, Pau Batlle Franch, Houman Owhadi +5
There are essentially three kinds of approaches to Uncertainty Quantification (UQ): (A) robust optimization, (B) Bayesian, (C) decision theory. Although (A) is robust, it is unfavo…
Decision Theoretic Bootstrapping
Peyman Tavallali, Hamed Hamze Bajgiran, Danial J. Esaid +1
The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution. Although t…