2 citations · 2 across the 1 of their papers we have counts for
2 papers
cs.LG2021★ 2 cited
Multi-task problems are not multi-objective
Michael Ruchte, Josif Grabocka
Multi-objective optimization (MOO) aims at finding a set of optimal configurations for a given set of objectives. A recent line of work applies MOO methods to the typical Machine L…
cs.LG2021
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
Michael Ruchte, Josif Grabocka
Multi-objective optimization (MOO) is a prevalent challenge for Deep Learning, however, there exists no scalable MOO solution for truly deep neural networks. Prior work either dema…