62 citations · 197 across the 20 of their papers we have counts for
21 papers
Label Smoothing Improves Machine Unlearning
Zonglin Di, Zhaowei Zhu, Jinghan Jia +6
The objective of machine unlearning (MU) is to eliminate previously learned data from a model. However, it is challenging to strike a balance between computation cost and performan…
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
Xinyuan Ji, Zhaowei Zhu, Wei Xi +4
Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The h…
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Jinlong Pang, Jialu Wang, Zhaowei Zhu +3
The pursuit of fairness in machine learning (ML), ensuring that the models do not exhibit biases toward protected demographic groups, typically results in a compromise scenario. Th…
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Zhaowei Zhu, Jialu Wang, Hao Cheng +1
Language models have shown promise in various tasks but can be affected by undesired data during training, fine-tuning, or alignment. For example, if some unsafe conversations are…
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu +4
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning. Most prior works treat either problem in an i…
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Zhaowei Zhu, Yuanshun Yao, Jiankai Sun +2
Evaluating fairness can be challenging in practice because the sensitive attributes of data are often inaccessible due to privacy constraints. The go-to approach that the industry…
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo +3
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separa…
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu, Jialu Wang, Yang Liu
The label noise transition matrix, denoting the transition probabilities from clean labels to noisy labels, is crucial for designing statistically robust solutions. Existing estima…
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng +3
Existing research on learning with noisy labels mainly focuses on synthetic label noise. Synthetic noise, though has clean structures which greatly enabled statistical analyses, of…
Mitigating Memorization of Noisy Labels via Regularization between Representations
Hao Cheng, Zhaowei Zhu, Xing Sun +1
Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs).…
Detecting Corrupted Labels Without Training a Model to Predict
Zhaowei Zhu, Zihao Dong, Yang Liu
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization of deep neural networks (DNNs). It is critical to find efficient ways to detect…
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Zhaowei Zhu, Tianyi Luo, Yang Liu
Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limite…
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.…
Learn and Pick Right Nodes to Offload
Zhaowei Zhu, Ting Liu, Shengda Jin +1
Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficient…
Interconnection Strategies for Self-Calibration of Large Scale Antenna Arrays
Hanyu Zhu, Fuqian Yang, Zhaowei Zhu +1
In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI…
Optimal Time Reuse in Cooperative D2D Relaying Networks
Zhaowei Zhu, Shengda Jin, Yu Zeng +2
Device-to-device (D2D) communication has become one important part of the 5G cellular networks particularly due to the booming of proximity-based applications, e.g. D2D relays. How…