10 citations · 51 across the 19 of their papers we have counts for
11 papers · 1 filter
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…
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…
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
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,…
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation
Sibo Zhu, Handong Zhao, Hongfu Liu
Outlier detection is one of the most popular and continuously rising topics in the data mining field due to its crucial academic value and extensive industrial applications. Among…
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…
Fairness-Aware Unsupervised Feature Selection
Xiaoying Xing, Hongfu Liu, Chen Chen +1
Feature selection is a prevalent data preprocessing paradigm for various learning tasks. Due to the expensive cost of acquiring supervision information, unsupervised feature select…
Learnable Subspace Clustering
Jun Li, Hongfu Liu, Zhiqiang Tao +2
This paper studies the large-scale subspace clustering (LSSC) problem with million data points. Many popular subspace clustering methods cannot directly handle the LSSC problem alt…
Consensus Clustering: An Embedding Perspective, Extension and Beyond
Hongfu Liu, Zhiqiang Tao, Zhengming Ding
Consensus clustering fuses diverse basic partitions (i.e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing…
Predictive Local Smoothness for Stochastic Gradient Methods
Jun Li, Hongfu Liu, Bineng Zhong +2
Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this pro…
Clustering with Outlier Removal
Hongfu Liu, Jun Li, Yue Wu +1
Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defi…