Search Results for author: Li Xiong

Found 31 papers, 9 papers with code

PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy

no code implementations22 Oct 2021 Xiaolan Gu, Ming Li, Li Xiong

In this paper, we develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.

Federated Learning Model Poisoning +1

Bit-aware Randomized Response for Local Differential Privacy in Federated Learning

no code implementations29 Sep 2021 Phung Lai, Hai Phan, Li Xiong, Khang Phuc Tran, My Thai, Tong Sun, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Rajiv Jain

In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).

Federated Learning Image Classification

Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation

no code implementations29 Sep 2021 Pengfei Tang, Wenjie Wang, Xiaolan Gu, Jian Lou, Li Xiong, Ming Li

To solve this challenge, a reconstruction network is built before the public pre-trained classifiers to offer certified robustness and defend against adversarial examples through input perturbation.

Image Classification

Communication Efficient Tensor Factorization for Decentralized Healthcare Networks

no code implementations3 Sep 2021 Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Sivasubramanium Bhavani, Joyce C. Ho

Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.

Computational Phenotyping

Temporal Network Embedding via Tensor Factorization

no code implementations22 Aug 2021 Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Joyce C. Ho

Representation learning on static graph-structured data has shown a significant impact on many real-world applications.

Link Prediction Network Embedding +1

SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling

no code implementations21 Aug 2021 Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi

Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.

Data Augmentation Federated Learning

Integer-arithmetic-only Certified Robustness for Quantized Neural Networks

no code implementations ICCV 2021 Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi

Adversarial data examples have drawn significant attention from the machine learning and security communities.


Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks

1 code implementation9 Aug 2021 Fereshteh Razmi, Li Xiong

Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted data into training data set, thus increasing the machine learning model's test error.

Classification Data Poisoning

RobustFed: A Truth Inference Approach for Robust Federated Learning

no code implementations18 Jul 2021 Farnaz Tahmasebian, Jian Lou, Li Xiong

Federated learning is a prominent framework that enables clients (e. g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy.

Federated Learning

Federated Graph Classification over Non-IID Graphs

no code implementations NeurIPS 2021 Han Xie, Jing Ma, Li Xiong, Carl Yang

Federated learning has emerged as an important paradigm for training machine learning models in different domains.

Classification Dynamic Time Warping +4

PAM: Understanding Product Images in Cross Product Category Attribute Extraction

no code implementations8 Jun 2021 Rongmei Lin, Xiang He, Jie Feng, Nasser Zalmout, Yan Liang, Li Xiong, Xin Luna Dong

Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph.

Optical Character Recognition Question Answering +1

Certified Robustness to Word Substitution Attack with Differential Privacy

no code implementations NAACL 2021 Wenjie Wang, Pengfei Tang, Jian Lou, Li Xiong

The robustness and security of natural language processing (NLP) models are significantly important in real-world applications.

Adversarial Robustness Classification +2

View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data

no code implementations NAACL (SMM4H) 2021 Payam Karisani, Jinho D. Choi, Li Xiong

Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view.

Word Embeddings

Learning with Hyperspherical Uniformity

1 code implementation2 Mar 2021 Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller

Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.

Inductive Bias L2 Regularization

Transparent Contribution Evaluation for Secure Federated Learning on Blockchain

no code implementations26 Jan 2021 Shuaicheng Ma, Yang Cao, Li Xiong

In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution.

Federated Learning

Generative Fairness Teaching

no code implementations1 Jan 2021 Rongmei Lin, Hanjun Dai, Li Xiong, Wei Wei

We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training.


Spatio-Temporal Tensor Sketching via Adaptive Sampling

no code implementations21 Jun 2020 Jing Ma, Qiuchen Zhang, Joyce C. Ho, Li Xiong

In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure.

PGLP: Customizable and Rigorous Location Privacy through Policy Graph

3 code implementations4 May 2020 Yang Cao, Yonghui Xiao, Shun Takagi, Li Xiong, Masatoshi Yoshikawa, Yilin Shen, Jinfei Liu, Hongxia Jin, Xiaofeng Xu

Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.

Cryptography and Security Computers and Society

PANDA: Policy-aware Location Privacy for Epidemic Surveillance

3 code implementations1 May 2020 Yang Cao, Shun Takagi, Yonghui Xiao, Li Xiong, Masatoshi Yoshikawa

Our system has three primary functions for epidemic surveillance: location monitoring, epidemic analysis, and contact tracing.

Databases Cryptography and Security

Orthogonal Over-Parameterized Training

1 code implementation CVPR 2021 Weiyang Liu, Rongmei Lin, Zhen Liu, James M. Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller

The inductive bias of a neural network is largely determined by the architecture and the training algorithm.

Inductive Bias

Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

no code implementations26 Aug 2019 Jing Ma, Qiuchen Zhang, Jian Lou, Joyce C. Ho, Li Xiong, Xiaoqian Jiang

We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.

Computational Phenotyping Privacy Preserving

Regularizing Neural Networks via Minimizing Hyperspherical Energy

1 code implementation CVPR 2020 Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song

Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.

Visually-aware Recommendation with Aesthetic Features

no code implementations2 May 2019 Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin

While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.

Decision Making Recommendation Systems +1

Aesthetic-based Clothing Recommendation

no code implementations16 Sep 2018 Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin

Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.

Recommendation Systems

Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations

2 code implementations29 Nov 2017 Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao, Li Xiong

Our analysis reveals that, the event-level privacy loss of a DP mechanism may \textit{increase over time}.


Quantifying Differential Privacy under Temporal Correlations

2 code implementations24 Oct 2016 Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao, Li Xiong

Our analysis reveals that the privacy leakage of a DP mechanism may accumulate and increase over time.

Databases Cryptography and Security

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