3 papers
cs.LG2026
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.LG2025
Enhancing Adversarial Robustness through Multi-Objective Representation Learning
Sedjro Salomon Hotegni, Sebastian Peitz
Deep neural networks (DNNs) are vulnerable to small adversarial perturbations, which are tiny changes to the input data that appear insignificant but cause the model to produce dra…
cs.LG2024
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…