b'Xiaolin Zheng'

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

∙ 07/18/2023

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

∙ 05/11/2023

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

∙ 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

∙ 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

∙ 04/19/2022

Partial Relaxed Optimal Transport for Denoised Recommendation

The interaction data used by recommender systems (RSs) inevitably includ...

0 Yanchao Tan, 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

∙ 02/10/2022

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

∙ 09/15/2021

Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation

Quality Estimation (QE) plays an essential role in applications of Machi...

7 Ke Wang, 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

∙ 03/27/2021

Multi-Facet Recommender Networks with Spherical Optimization

Implicit feedback is widely explored by modern recommender systems. Sinc...

0 Yanchao Tan, 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

∙ 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

∙ 03/05/2020

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

∙ 11/13/2019

Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling

As the cornerstone of modern portfolio theory, Markowitz's mean-variance...

0 Mengying Zhu, et al. ∙

research

∙ 11/13/2019

Context-aware Dynamic Assets Selection for Online Portfolio Selection based on Contextual Bandit

Online portfolio selection is a sequential decision-making problem in fi...

0 Mengying Zhu, 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

∙ 04/28/2018

User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning

This paper considers recommendation algorithm ensembles in a user-sensit...

0 Menghan Wang, et al. ∙

research

∙ 12/25/2017

Collaborative Autoencoder for Recommender Systems

In recent years, deep neural networks have yielded state-of-the-art perf...

0 Qibing Li, et al. ∙

research

∙ 12/25/2017

Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback

In recent years, deep neural networks have yielded state-of-the-art perf...

0 Qibing Li, et al. ∙

research

∙ 11/30/2017

Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation

This paper is concerned with how to make efficient use of social informa...

0 Menghan Wang, et al. ∙

Xiaolin Zheng

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