4 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Multi-Objective Optimization for Sparse Deep Multi-Task Learning
S. S. Hotegni, M. Berkemeier, S. Peitz
Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning)…
Approximation Algorithms for Fair Range Clustering
Sèdjro S. Hotegni, Sepideh Mahabadi, Ali Vakilian
This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick centers with the minimum clustering…
Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases
Sèdjro Salomon Hotegni, Ernest Fokoué
Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage a…