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Found 4 papers, 4 papers with code
Scalarisation-based risk concepts for robust multi-objective optimisation
Robustification refers to the strategy that is used to marginalise over the uncertainty in the problem.
Random Pareto front surfaces
As a motivating example, we investigate how these statistics can be used within a design of experiments setting, where the goal is to both infer and use the Pareto front surface distribution in order to make effective decisions.
Multi-objective optimisation via the R2 utilities
As part of our work, we show that these utilities are monotone and submodular set functions which can be optimised effectively using greedy optimisation algorithms.
Joint Entropy Search for Multi-objective Bayesian Optimization
Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives.
