4 citations · 4 across the 2 of their papers we have counts for
8 papers
You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
Xiaotian Han, Tianlong Chen, Kaixiong Zhou +3
Deep neural networks are prone to various bias issues, jeopardizing their applications for high-stake decision-making. Existing fairness methods typically offer a fixed accuracy-fa…
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang +5
It is well known that LLMs cannot generalize well to long contexts whose lengths are larger than the training sequence length. This poses challenges when employing LLMs for process…
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…
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
Hengrui Gu, Kaixiong Zhou, Xiaotian Han +3
Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achie…
Marginal Nodes Matter: Towards Structure Fairness in Graphs
Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang +3
In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness w…