Web: http://arxiv.org/abs/2209.07245

Sept. 16, 2022, 1:12 a.m. | Eric Enouen, Katja Mathesius, Sean Wang, Arielle Carr, Sihong Xie

cs.LG updates on arXiv.org arxiv.org

Multiple-objective optimization (MOO) aims to simultaneously optimize
multiple conflicting objectives and has found important applications in machine
learning, such as minimizing classification loss and discrepancy in treating
different populations for fairness. At optimality, further optimizing one
objective will necessarily harm at least another objective, and decision-makers
need to comprehensively explore multiple optima (called Pareto front) to
pinpoint one final solution. We address the efficiency of finding the Pareto
front. First, finding the front from scratch using stochastic multi-gradient
descent (SMGD) …

arxiv detection misinformation optimization

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