3 code implementations • 10 Dec 2018 • Zhengyang Wang, Na Zou, Dinggang Shen, Shuiwang Ji
In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.
no code implementations • 11 Aug 2019 • Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou
SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.
no code implementations • 23 Aug 2019 • Mengnan Du, Fan Yang, Na Zou, Xia Hu
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.
1 code implementation • 7 Oct 2019 • Yuening Li, Daochen Zha, Na Zou, Xia Hu
PyODDS is an end-to end Python system for outlier detection with database support.
no code implementations • 17 Dec 2019 • Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu
To further improve the graph representation learning ability, hierarchical GNN has been explored.
no code implementations • 12 Mar 2020 • Yuening Li, Daochen Zha, Praveen Kumar Venugopal, Na Zou, Xia Hu
Outlier detection is an important task for various data mining applications.
1 code implementation • 15 Jun 2020 • Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, Xia Hu
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
no code implementations • 21 Aug 2020 • Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu
Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.
no code implementations • 14 Apr 2021 • Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu
The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.
no code implementations • 28 May 2021 • Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu
However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.
1 code implementation • 9 Aug 2021 • Daochen Zha, Zaid Pervaiz Bhat, Yi-Wei Chen, Yicheng Wang, Sirui Ding, Jiaben Chen, Kwei-Herng Lai, Mohammad Qazim Bhat, Anmoll Kumar Jain, Alfredo Costilla Reyes, Na Zou, Xia Hu
Action recognition is an important task for video understanding with broad applications.
no code implementations • 4 Nov 2021 • Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou
Machine learning models are becoming pervasive in high-stakes applications.
no code implementations • 8 Nov 2021 • Ruixiang Tang, Ninghao Liu, Fan Yang, Na Zou, Xia Hu
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications.
no code implementations • 8 Feb 2022 • Zhimeng Jiang, Xiaotian Han, Chao Fan, Zirui Liu, Na Zou, Ali Mostafavi, Xia Hu
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
no code implementations • 29 Jun 2022 • Qizhang Feng, Mengnan Du, Na Zou, Xia Hu
The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare.
1 code implementation • 20 Jul 2022 • Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu
Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.
1 code implementation • 5 Aug 2022 • Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).
no code implementations • 25 Aug 2022 • Mengnan Du, Fengxiang He, Na Zou, DaCheng Tao, Xia Hu
We first introduce the concepts of shortcut learning of language models.
2 code implementations • 26 Aug 2022 • Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan, Sirui Ding, Na Zou, Xia Hu
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 26 Nov 2022 • Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang, Mengnan Du, Chia-Yuan Chang, Na Zou, Xia Hu
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.
1 code implementation • 3 Jan 2023 • Yushun Dong, Binchi Zhang, Yiling Yuan, Na Zou, Qi Wang, Jundong Li
Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i. e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i. e., the teacher GNN model).
1 code implementation • 31 Jan 2023 • Xiaotian Han, Zhimeng Jiang, Hongye Jin, Zirui Liu, Na Zou, Qifan Wang, Xia Hu
Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds.
no code implementations • 18 Feb 2023 • Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
no code implementations • 2 Mar 2023 • Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang
Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output.
1 code implementation • NeurIPS 2023 • Zhimeng Jiang, Xiaotian Han, Hongye Jin, Guanchu Wang, Rui Chen, Na Zou, Xia Hu
Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group.
no code implementations • 19 Mar 2023 • Shenghan Zhang, Haoxuan Li, Ruixiang Tang, Sirui Ding, Laila Rasmy, Degui Zhi, Na Zou, Xia Hu
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
no code implementations • 24 Mar 2023 • Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
no code implementations • 30 Mar 2023 • Sirui Ding, Qiaoyu Tan, Chia-Yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, Xia Hu
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
1 code implementation • 11 Jun 2023 • Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou
We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks.
1 code implementation • 15 Jun 2023 • Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods.
no code implementations • 9 Jul 2023 • Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han, Xia Hu, Na Zou
Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias.
no code implementations • 14 Jul 2023 • Chia-Yuan Chang, Yu-Neng Chuang, Guanchu Wang, Mengnan Du, Na Zou
Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains.
no code implementations • 23 Sep 2023 • Yicheng Wang, Xiaotian Han, Leisheng Yu, Na Zou
Through our investigation, the discrepancy between the elderly and the young arises due to 1) an imbalanced dataset and 2) the milder symptoms often seen in early-onset patients.
no code implementations • 2 Oct 2023 • Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang, Kwei-Herng Lai, Anxiao Jiang, Na Zou
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the "concept drift".
no code implementations • 23 Oct 2023 • Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou
Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.
1 code implementation • 19 Dec 2023 • Zhimeng Jiang, Xiaotian Han, Chao Fan, Zirui Liu, Na Zou, Ali Mostafavi, Xia Hu
To this end, we aim to achieve fairness via a new GNN architecture.