Search Results for author: Lingyang Chu

Found 14 papers, 3 papers with code

TabularMark: Watermarking Tabular Datasets for Machine Learning

no code implementations21 Jun 2024 Yihao Zheng, Haocheng Xia, Junyuan Pang, Jinfei Liu, Kui Ren, Lingyang Chu, Yang Cao, Li Xiong

Data noise partitioning is utilized for data perturbation during embedding, which is adaptable for numerical and categorical attributes while preserving the data utility.

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices

no code implementations1 Mar 2023 Qiying Pan, Yifei Zhu, Lingyang Chu

In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs.

Federated Learning Graph Learning

Mining Minority-class Examples With Uncertainty Estimates

no code implementations15 Dec 2021 Gursimran Singh, Lingyang Chu, Lanjun Wang, Jian Pei, Qi Tian, Yong Zhang

In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes.

Achieving Model Fairness in Vertical Federated Learning

1 code implementation17 Sep 2021 Changxin Liu, Zhenan Fan, Zirui Zhou, Yang Shi, Jian Pei, Lingyang Chu, Yong Zhang

To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.

BIG-bench Machine Learning Fairness +2

FedFair: Training Fair Models In Cross-Silo Federated Learning

no code implementations13 Sep 2021 Lingyang Chu, Lanjun Wang, Yanjie Dong, Jian Pei, Zirui Zhou, Yong Zhang

In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party.

Fairness Federated Learning

Finding Representative Interpretations on Convolutional Neural Networks

no code implementations ICCV 2021 Peter Cho-Ho Lam, Lingyang Chu, Maxim Torgonskiy, Jian Pei, Yong Zhang, Lanjun Wang

Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models.

Robust Counterfactual Explanations on Graph Neural Networks

no code implementations NeurIPS 2021 Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, Yong Zhang

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition.


Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test

no code implementations1 Nov 2020 Zicun Cong, Lingyang Chu, Yu Yang, Jian Pei

One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test.

Anomaly Detection Astronomy +2

Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

1 code implementation17 Jun 2019 Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu, Jian Pei

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

no code implementations17 Feb 2018 Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.

Finding Theme Communities from Database Networks

no code implementations23 Sep 2017 Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang, Enhong Chen

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.

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