Multi-Objective Machine Learning

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How researchers see this paper

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Real-Time Federated Evolutionary Neural Architecture Search (2020, neutral): “By contrast, the Pareto approach to solving multi-objective optimization problems has been very popular and successful in evolutionary computation [43], which has also been extended to machine learning [44].”
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies (2008, background): “For an updated and more detailed account of the existing research on multiobjective learning, the reader is referred to [18].”
Machine learning with operational costs (2011, background): “In Bayesian decision theory, the prior is over possible probabilistic models, rather than on possible costs as in this paper.”
A Multi-Facet Survey on Memetic Computation (2011, methodology): “In the domain of multiobjective optimization (MOO) [7], [33], [48], the more popular population-based methods include the non-dominated sorting genetic algorithm-II [62], evolutionary multiobjective optimization algorithms [27], Pareto archived evolutionary strategy [63], multiobjective PSO [64], et”