44 citations · 130 across the 14 of their papers we have counts for
7 papers · 1 filter
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Huiyuan Chen, Vivian Lai, Hongye Jin +3
Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Informa…
Chasing Fairness in Graphs: A GNN Architecture Perspective
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strateg…
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length
Hongye Jin, Xiaotian Han, Jingfeng Yang +3
The evolving sophistication and intricacies of Large Language Models (LLMs) yield unprecedented advancements, yet they simultaneously demand considerable computational resources an…
On the Equivalence of Graph Convolution and Mixup
Xiaotian Han, Hanqing Zeng, Yu Chen +9
This paper investigates the relationship between graph convolution and Mixup techniques. Graph convolution in a graph neural network involves aggregating features from neighboring…
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
Xiaotian Han, Jianfeng Chi, Yu Chen +4
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is import…
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Zhimeng Jiang, Xiaotian Han, Hongye Jin +4
Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well u…
Retiring DP: New Distribution-Level Metrics for Demographic Parity
Xiaotian Han, Zhimeng Jiang, Hongye Jin +4
Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to…