367 citations · 452 across the 25 of their papers we have counts for
5 papers · 1 filter
MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
Wei Chen, Faez Ahmed
Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) ge…
How Does Data Augmentation Affect Privacy in Machine Learning?
Da Yu, Huishuai Zhang, Wei Chen +2
It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposi…
Combinatorial Pure Exploration of Dueling Bandit
Wei Chen, Yihan Du, Longbo Huang +1
In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each…
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
Yihan Du, Yuko Kuroki, Wei Chen
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \…
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
Wei Chen, Kartikeya Bhardwaj, Radu Marculescu
In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data being non-IID) across multipl…