12 citations · 13 across the 3 of their papers we have counts for
8 papers
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Zhaowei Zhu, Yiwen Song, Yang Liu
The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy…
A Second-Order Approach to Learning with Instance-Dependent Label Noise
Zhaowei Zhu, Tongliang Liu, Yang Liu
The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined…
Federated Bandit: A Gossiping Approach
Zhaowei Zhu, Jingxuan Zhu, Ji Liu +1
In this paper, we study \emph{Federated Bandit}, a decentralized Multi-Armed Bandit problem with a set of agents, who can only communicate their local data with neighbors descr…
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Hao Cheng, Zhaowei Zhu, Xingyu Li +3
Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literatu…
Policy Learning Using Weak Supervision
Jingkang Wang, Hongyi Guo, Zhaowei Zhu +1
Most existing policy learning solutions require the learning agents to receive high-quality supervision signals such as well-designed rewards in reinforcement learning (RL) or high…
Online optimal task offloading with one-bit feedback
Shangshu Zhao, Zhaowei Zhu, Fuqian Yang +1
Task offloading is an emerging technology in fog-enabled networks. It allows users to transmit tasks to neighbor fog nodes so as to utilize the computing resources of the networks.…