4 citations · 8 across the 7 of their papers we have counts for
11 papers
How to Sell High-Dimensional Data Optimally
Andrew Li, R. Ravi, Karan Singh +2
Motivated by the problem of selling large, proprietary data, we consider an information pricing problem proposed by Bergemann et al. that involves a decision-making buyer and a mon…
Normalizing Flow with Variational Latent Representation
Hanze Dong, Shizhe Diao, Weizhong Zhang +1
Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions. However, the standar…
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Finding Dynamics Preserving Adversarial Winning Tickets
Xupeng Shi, Pengfei Zheng, A. Adam Ding +2
Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and adversarial training has been shown to be a promising method for improving the adversarial robustness o…
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Xiao Zhou, Weizhong Zhang, Hang Xu +1
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative impo…