20 citations · 49 across the 19 of their papers we have counts for
5 papers · 1 filter
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Mingjing Xu, Peizhong Ju, Jia Liu +1
Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning,…
Adversarial Attacks to Multi-Modal Models
Zhihao Dou, Xin Hu, Haibo Yang +2
Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superi…
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?
Peizhong Ju, Haibo Yang, Jia Liu +2
Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. W…
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Haibo Yang, Peiwen Qiu, Prashant Khanduri +2
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been obse…
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Tianchen Zhou, FNU Hairi, Haibo Yang +5
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This pape…