103 citations · 515 across the 26 of their papers we have counts for
6 papers · 1 filter
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Pengyu Zhang, Yingbo Zhou, Ming Hu +2
Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Si…
Online Structured Meta-learning
Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi +3
Learning quickly is of great importance for machine intelligence deployed in online platforms. With the capability of transferring knowledge from learned tasks, meta-learning has s…
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
Junwen Bai, Weiran Wang, Yingbo Zhou +1
We propose Deep Autoencoding Predictive Components (DAPC) -- a self-supervised representation learning method for sequence data, based on the intuition that useful representations…
Differentially Private Deep Learning with Smooth Sensitivity
Lichao Sun, Yingbo Zhou, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is th…
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu +2
Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent…
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong +1
Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundan…