49 citations · 408 across the 42 of their papers we have counts for
6 papers · 1 filter
DP-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang, Guojun Zhang, Mahdi Karami +3
Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face imag…
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
Ji Xin, Raphael Tang, Zhiying Jiang +2
There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider…
Towards Explanation for Unsupervised Graph-Level Representation Learning
Qinghua Zheng, Jihong Wang, Minnan Luo +4
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the inpu…
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
Artur Back de Luca, Guojun Zhang, Xi Chen +1
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models. In this setting, one m…
Indiscriminate Data Poisoning Attacks on Neural Networks
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting "poisoned" data into the training process, have attracted significant recent attention…
Proportional Fairness in Federated Learning
Guojun Zhang, Saber Malekmohammadi, Xi Chen +1
With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory p…