27 citations · 52 across the 12 of their papers we have counts for
12 papers
Dual Node and Edge Fairness-Aware Graph Partition
Tingwei Liu, Peizhao Li, Hongfu Liu
Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typic…
Learning Antidote Data to Individual Unfairness
Peizhao Li, Ethan Xia, Hongfu Liu
Fairness is essential for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, deriving from a consensus that `similar ind…
Characterizing the Influence of Graph Elements
Zizhang Chen, Peizhao Li, Hongfu Liu +1
Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of tra…
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra +1
Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair…
Exploiting Temporal Relations on Radar Perception for Autonomous Driving
Peizhao Li, Pu Wang, Karl Berntorp +1
We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather condi…
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li, Hongfu Liu
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper,…
Deep Clustering based Fair Outlier Detection
Hanyu Song, Peizhao Li, Hongfu Liu
In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitl…
SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen +5
We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework…
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Peizhao Li, Zhengming Ding, Hongfu Liu
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minim…
Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
Xiaolong Jiang, Peizhao Li, Yanjing Li +1
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame dat…
Two-Stream Multi-Task Network for Fashion Recognition
Peizhao Li, Yanjing Li, Xiaolong Jiang +1
In this paper, we present a two-stream multi-task network for fashion recognition. This task is challenging as fashion clothing always contain multiple attributes, which need to be…
Model-free Tracking with Deep Appearance and Motion Features Integration
Xiaolong Jiang, Peizhao Li, Xiantong Zhen +1
Being able to track an anonymous object, a model-free tracker is comprehensively applicable regardless of the target type. However, designing such a generalized framework is challe…