b'Chaochao Chen'

research

∙ 09/04/2023

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, ...

0 Zhongxuan Han, et al. ∙

research

∙ 08/18/2023

Defending Label Inference Attacks in Split Learning under Regression Setting

As a privacy-preserving method for implementing Vertical Federated Learn...

0 Haoze Qiu, et al. ∙

research

∙ 08/17/2023

Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data

Federated learning (FL) is a distributed machine learning paradigm that ...

0 Xinting Liao, et al. ∙

research

∙ 08/15/2023

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without v...

0 Xiaolin Zheng, et al. ∙

research

∙ 08/15/2023

Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in p...

0 Xiaolin Zheng, et al. ∙

research

∙ 07/26/2023

HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning

Federated learning (FL) collaboratively models user data in a decentrali...

0 Xinting Liao, et al. ∙

research

∙ 07/18/2023

Federated Large Language Model: A Position Paper

Large scale language models (LLM) have received significant attention an...

0 Chaochao Chen, et al. ∙

research

∙ 07/07/2023

Federated Unlearning via Active Forgetting

The increasing concerns regarding the privacy of machine learning models...

0 Yuyuan Li, et al. ∙

research

∙ 06/26/2023

Federated Learning on Non-iid Data via Local and Global Distillation

Most existing federated learning algorithms are based on the vanilla Fed...

0 Xiaolin Zheng, et al. ∙

research

∙ 05/29/2023

Reducing Communication for Split Learning by Randomized Top-k Sparsification

Split learning is a simple solution for Vertical Federated Learning (VFL...

0 Fei Zheng, et al. ∙

research

∙ 05/11/2023

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserv...

0 Xinting Liao, et al. ∙

research

∙ 04/20/2023

Selective and Collaborative Influence Function for Efficient Recommendation Unlearning

Recent regulations on the Right to be Forgotten have greatly influenced ...

0 Yuyuan Li, et al. ∙

research

∙ 02/13/2023

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

In recommendation scenarios, there are two long-standing challenges, i.e...

0 Feng Zhu, et al. ∙

research

∙ 02/06/2023

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

Spatio-temporal kriging is an important problem in web and social applic...

0 Chuanpan Zheng, et al. ∙

research

∙ 10/24/2022

Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems

Recommender systems are fundamental information filtering techniques to ...

0 Xiaolin Zheng, et al. ∙

research

∙ 10/18/2022

Making Split Learning Resilient to Label Leakage by Potential Energy Loss

As a practical privacy-preserving learning method, split learning has dr...

0 Fei Zheng, et al. ∙

research

∙ 09/21/2022

DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation

Sequential Recommendation (SR) characterizes evolving patterns of user b...

0 Xiaolin Zheng, et al. ∙

research

∙ 09/04/2022

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

Integrating multiple online social networks (OSNs) has important implica...

6 Jiaqian Ren, et al. ∙

research

∙ 05/24/2022

HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation

The ever-increasing data scale of user-item interactions makes it challe...

0 Fan Wang, et al. ∙

research

∙ 05/20/2022

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Deep graph learning has achieved remarkable progresses in both business ...

0 Bingzhe Wu, et al. ∙

research

∙ 05/13/2022

Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation

Cross-Domain Recommendation (CDR) has been popularly studied to utilize ...

0 Weiming Liu, et al. ∙

research

∙ 03/22/2022

Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation

Privacy laws and regulations enforce data-driven systems, e.g., recommen...

0 Yuyuan Li, et al. ∙

research

∙ 02/15/2022

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Social recommendation has shown promising improvements over traditional ...

0 Jamie Cui, et al. ∙

research

∙ 02/10/2022

Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation

Cross-Domain Recommendation (CDR) has been popularly studied to utilize ...

0 Weiming Liu, et al. ∙

research

∙ 02/10/2022

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

Cross Domain Recommendation (CDR) has been popularly studied to alleviat...

0 Chaochao Chen, et al. ∙

research

∙ 12/28/2021

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

In federated learning (FL) problems, client sampling plays a key role in...

0 Boxin Zhao, et al. ∙

research

∙ 12/14/2021

Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis

Recently, generalization bounds of the non-convex empirical risk minimiz...

0 Bingzhe Wu, et al. ∙

research

∙ 08/18/2021

A Unified Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) ...

0 Feng Zhu, et al. ∙

research

∙ 08/17/2021

Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation

With the increasing demands for privacy protection, privacy-preserving m...

0 Fei Zheng, et al. ∙

research

∙ 03/02/2021

Cross-Domain Recommendation: Challenges, Progress, and Prospects

To address the long-standing data sparsity problem in recommender system...

0 Feng Zhu, et al. ∙

research

∙ 12/17/2020

Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Deep Neural Networks (DNNs) have achieved remarkable progress in various...

0 Chaochao Chen, et al. ∙

research

∙ 11/23/2020

Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty

Federated learning (FL) has attracted increasing attention in recent yea...

0 Yilun Lin, et al. ∙

research

∙ 11/20/2020

Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications

Knowledge Graph (KG) has attracted more and more companies' attention fo...

0 Chaochao Chen, et al. ∙

research

∙ 11/06/2020

ASFGNN: Automated Separated-Federated Graph Neural Network

Graph Neural Networks (GNNs) have achieved remarkable performance by tak...

0 Longfei Zheng, et al. ∙

research

∙ 09/14/2020

A Deep Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR)...

0 Feng Zhu, et al. ∙

research

∙ 05/25/2020

Privacy-Preserving Graph Neural Network for Node Classification

Recently, Graph Neural Network (GNN) has achieved remarkable progresses ...

0 Jun Zhou, et al. ∙

research

∙ 03/12/2020

Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

Points of interest (POI) recommendation has been drawn much attention re...

0 Chaochao Chen, et al. ∙

research

∙ 03/05/2020

Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

Recently latent factor model (LFM) has been drawing much attention in re...

67 Chaochao Chen, et al. ∙

research

∙ 03/05/2020

Practical Privacy Preserving POI Recommendation

Point-of-Interest (POI) recommendation has been extensively studied and ...

0 Chaochao Chen, et al. ∙

research

∙ 02/27/2020

Heterogeneous Graph Neural Networks for Malicious Account Detection

We present, GEM, the first heterogeneous graph neural network approach f...

0 Ziqi Liu, et al. ∙

research

∙ 02/27/2020

Adapted tree boosting for Transfer Learning

Secure online transaction is an essential task for e-commerce platforms....

0 Wenjing Fang, et al. ∙

research

∙ 02/27/2020

How Much Can A Retailer Sell? Sales Forecasting on Tmall

Time-series forecasting is an important task in both academic and indust...

0 Chaochao Chen, et al. ∙

research

∙ 02/06/2020

Privacy Preserving PCA for Multiparty Modeling

In this paper, we present a general multiparty model-ing paradigm with P...

0 Yingting Liu, et al. ∙

research

∙ 02/06/2020

Secure Social Recommendation based on Secret Sharing

Nowadays, privacy preserving machine learning has been drawing much atte...

0 Chaochao Chen, et al. ∙

research

∙ 12/26/2019

A Time Attention based Fraud Transaction Detection Framework

With online payment platforms being ubiquitous and important, fraud tran...

0 Longfei Li, et al. ∙

research

∙ 10/12/2019

Quantification of the Leakage in Federated Learning

With the growing emphasis on users' privacy, federated learning has beco...

0 Zhaorui Li, et al. ∙

research

∙ 10/05/2019

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

Bayesian deep learning is recently regarded as an intrinsic way to chara...

0 Bingzhe Wu, et al. ∙

research

∙ 08/21/2019

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

In this paper, we aim to understand the generalization properties of gen...

0 Bingzhe Wu, et al. ∙

research

∙ 08/26/2018

FinBrain: When Finance Meets AI 2.0

Artificial intelligence (AI) is the core technology of technological rev...

0 Xiaolin Zheng, et al. ∙

research

∙ 05/11/2018

Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

Internet companies are facing the need of handling large scale machine l...

0 Ya-Lin Zhang, et al. ∙

Chaochao Chen

Featured Co-authors