3 citations · 3 across the 1 of their papers we have counts for
2 papers
cs.LG2025
SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion
Sedjro Salomon Hotegni, Sebastian Peitz
Developing efficient multi-objective optimization methods to compute the Pareto set of optimal compromises between conflicting objectives remains a key challenge, especially for la…
cs.LG2024★ 3 cited
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
Sebastian Peitz, Sedjro Salomon Hotegni
Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration…