Common pitfalls to avoid while using multiobjective optimization in machine learning

arXiv:2405.01480v1 Announce Type: new
Abstract: Recently, there has been an increasing interest in exploring the application of multiobjective optimization (MOO) in machine learning (ML). The interest is driven by the numerous situations in real-life applications where multiple objectives need to be optimized simultaneously. A key aspect of MOO is the existence of a Pareto set, rather than a single optimal solution, which illustrates the inherent trade-offs between objectives. Despite its potential, there is a noticeable lack of satisfactory literature that …

abstract application applications arxiv cs.lg key life machine machine learning math.oc multiple optimization type while