420 citations · 1.1k across the 45 of their papers we have counts for
10 papers · 1 filter
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Hangyu Zhu, Rui Wang, Yaochu Jin +1
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing th…
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Jinjin Xu, Yaochu Jin, Wenli Du
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorith…
Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu +1
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than…
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
Ye Tian, Xingyi Zhang, Cheng He +2
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators i…
Information Maximization Clustering via Multi-View Self-Labelling
Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Image clustering is a particularly challenging computer vision task, which aims to generate annotations without human supervision. Recent advances focus on the use of self-supervis…