Search Results for author: Na Zou

Found 36 papers, 13 papers with code

Non-local U-Net for Biomedical Image Segmentation

3 code implementations10 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.

Brain Image Segmentation Image Segmentation +2

SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

no code implementations11 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.

Anomaly Detection Density Estimation

Fairness in Deep Learning: A Computational Perspective

no code implementations23 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.

Decision Making Fairness

PyODDS: An End-to-End Outlier Detection System

1 code implementation7 Oct 2019 Yuening Li, Daochen Zha, Na Zou, Xia Hu

PyODDS is an end-to end Python system for outlier detection with database support.

BIG-bench Machine Learning Outlier Detection

Multi-Channel Graph Convolutional Networks

no code implementations17 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.

Clustering Graph Classification +1

Mitigating Gender Bias in Captioning Systems

1 code implementation15 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.

Benchmarking Gender Prediction +1

A Unified Taylor Framework for Revisiting Attribution Methods

no code implementations21 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.

Benchmarking Decision Making

Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution

no code implementations14 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.

Decision Making

A General Taylor Framework for Unifying and Revisiting Attribution Methods

no code implementations28 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.

Benchmarking Decision Making

Defense Against Explanation Manipulation

no code implementations8 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.

Adversarial Attack BIG-bench Machine Learning

FMP: Toward Fair Graph Message Passing against Topology Bias

no code implementations8 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.

Contrastive Learning Fairness +1

Fair Machine Learning in Healthcare: A Review

no code implementations29 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.

BIG-bench Machine Learning Fairness

Mitigating Algorithmic Bias with Limited Annotations

1 code implementation20 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.

Fairness

DIVISION: Memory Efficient Training via Dual Activation Precision

1 code implementation5 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).

Quantization

Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning

2 code implementations26 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

Mitigating Relational Bias on Knowledge Graphs

no code implementations26 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.

Graph Representation Learning Knowledge Graphs +1

RELIANT: Fair Knowledge Distillation for Graph Neural Networks

1 code implementation3 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).

Fairness Graph Learning +1

Retiring $Δ$DP: New Distribution-Level Metrics for Demographic Parity

1 code implementation31 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.

Fairness

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning

no code implementations18 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.

Fairness Knowledge Distillation

Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions

no code implementations2 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.

Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

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.

Attribute Fairness

PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data

no code implementations19 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.

Ensemble Learning

Graph Mixup with Soft Alignments

1 code implementation11 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.

Data Augmentation Graph Classification

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods

1 code implementation15 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.

Benchmarking Fairness

Towards Assumption-free Bias Mitigation

no code implementations9 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.

valid

DISPEL: Domain Generalization via Domain-Specific Liberating

no code implementations14 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.

Domain Generalization

Beyond Fairness: Age-Harmless Parkinson's Detection via Voice

no code implementations23 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.

Fairness

CODA: Temporal Domain Generalization via Concept Drift Simulator

no code implementations2 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".

Domain Generalization Feature Correlation

Marginal Nodes Matter: Towards Structure Fairness in Graphs

no code implementations23 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.

Fairness

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