b'Bolin Ding'

research

∙ 05/04/2023

Efficient Personalized Federated Learning via Sparse Model-Adaptation

Federated Learning (FL) aims to train machine learning models for multip...

0 Daoyuan Chen, et al. ∙

research

∙ 03/23/2023

FS-Real: Towards Real-World Cross-Device Federated Learning

Federated Learning (FL) aims to train high-quality models in collaborati...

0 Daoyuan Chen, et al. ∙

research

∙ 02/03/2023

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

In this work, besides improving prediction accuracy, we study whether pe...

0 Zeyu Qin, et al. ∙

research

∙ 12/12/2022

Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning

The increasing privacy concerns on personal private text data promote th...

0 Chenhe Dong, et al. ∙

research

∙ 06/13/2022

Towards Universal Sequence Representation Learning for Recommender Systems

In order to develop effective sequential recommenders, a series of seque...

0 Yupeng Hou, et al. ∙

research

∙ 06/11/2022

Sampling-based Estimation of the Number of Distinct Values in Distributed Environment

In data mining, estimating the number of distinct values (NDV) is a fund...

0 Jiajun Li, et al. ∙

research

∙ 06/08/2022

FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization

Hyperparameter optimization (HPO) is crucial for machine learning algori...

0 Zhen Wang, et al. ∙

research

∙ 06/08/2022

pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

Personalized Federated Learning (pFL), which utilizes and deploys distin...

0 Daoyuan Chen, et al. ∙

research

∙ 06/07/2022

Federated Hetero-Task Learning

To investigate the heterogeneity of federated learning in real-world sce...

0 Liuyi Yao, et al. ∙

research

∙ 06/06/2022

ID-Agnostic User Behavior Pre-training for Sequential Recommendation

Recently, sequential recommendation has emerged as a widely studied topi...

0 Shanlei Mu, et al. ∙

research

∙ 05/27/2022

EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

Graph Neural Networks (GNNs) have received extensive research attention ...

0 Runlin Lei, et al. ∙

research

∙ 04/12/2022

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

The incredible development of federated learning (FL) has benefited vari...

0 Zhen Wang, et al. ∙

research

∙ 04/11/2022

FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing

Although remarkable progress has been made by the existing federated lea...

0 Yuexiang Xie, et al. ∙

research

∙ 04/01/2022

Proactively Control Privacy in Recommender Systems

Recently, privacy issues in web services that rely on users' personal da...

0 Ziqian Chen, et al. ∙

research

∙ 02/06/2022

Learning to be a Statistician: Learned Estimator for Number of Distinct Values

Estimating the number of distinct values (NDV) in a column is useful for...

0 Renzhi Wu, et al. ∙

research

∙ 01/18/2022

Recommendation Unlearning

Recommender systems provide essential web services by learning users' pe...

0 Chong Chen, et al. ∙

research

∙ 12/27/2021

Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling

Recently, Product Question Answering (PQA) on E-Commerce platforms has a...

5 Yang Deng, et al. ∙

research

∙ 08/30/2021

Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation

Despite significant progress has been achieved in text summarization, fa...

0 Yuexiang Xie, et al. ∙

research

∙ 05/28/2021

CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation

To improve user experience and profits of corporations, modern industria...

0 Xu Xie, et al. ∙

research

∙ 05/20/2021

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

Conversational recommender systems (CRS) enable the traditional recommen...

0 Yang Deng, et al. ∙

research

∙ 03/05/2021

Competitive Information Disclosure with Multiple Receivers

This paper analyzes a model of competition in Bayesian persuasion in whi...

0 Bolin Ding, et al. ∙

research

∙ 01/09/2021

FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data

Interactive response time is important in analytical pipelines for users...

22 Shuyuan Yan, et al. ∙

research

∙ 01/04/2021

A Pluggable Learned Index Method via Sampling and Gap Insertion

Database indexes facilitate data retrieval and benefit broad application...

0 Yaliang Li, et al. ∙

research

∙ 10/27/2020

Contrastive Pre-training for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommen...

0 Xu Xie, et al. ∙

research

∙ 09/11/2020

Intertwining Order Preserving Encryption and Differential Privacy

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the ...

0 Amrita Roy Chowdhury, et al. ∙

research

∙ 07/29/2020

Interactive Feature Generation via Learning Adjacency Tensor of Feature Graph

To automate the generation of interactive features, recent methods are p...

3 Yuexiang Xie, et al. ∙

research

∙ 07/04/2020

Simple and Deep Graph Convolutional Networks

Graph convolutional networks (GCNs) are a powerful deep learning approac...

0 Ming Chen, et al. ∙

research

∙ 05/21/2020

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of seq...

0 Ruiyang Ren, et al. ∙

research

∙ 04/07/2020

Practical Data Poisoning Attack against Next-Item Recommendation

Online recommendation systems make use of a variety of information sourc...

0 Hengtong Zhang, et al. ∙

research

∙ 01/13/2020

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

Large pre-trained language models such as BERT have shown their effectiv...

0 Daoyuan Chen, et al. ∙

research

∙ 01/03/2020

Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, me...

6 Huaxiu Yao, et al. ∙

research

∙ 09/25/2019

Design of Algorithms under Policy-Aware Local Differential Privacy: Utility-Privacy Trade-offs

Local differential privacy (LDP) enables private data sharing and analyt...

0 Zhuolun Xiang, et al. ∙

research

∙ 08/30/2019

MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

0 Tianhao Wang, et al. ∙

research

∙ 08/30/2019

Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

0 Tianhao Wang, et al. ∙

research

∙ 03/01/2019

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

Continuous integration is an indispensable step of modern software engin...

0 Cedric Renggli, et al. ∙

research

∙ 11/08/2018

Efficient Identification of Approximate Best Configuration of Training in Large Datasets

A configuration of training refers to the combinations of feature engine...

0 Silu Huang, et al. ∙

research

∙ 10/10/2018

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

Nowadays, crowd sensing becomes increasingly more popular due to the ubi...

0 Yaliang Li, et al. ∙

research

∙ 07/02/2018

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Differential privacy has emerged as the main definition for private data...

0 Joshua Allen, et al. ∙

research

∙ 03/24/2018

Comparing Population Means under Local Differential Privacy: with Significance and Power

A statistical hypothesis test determines whether a hypothesis should be ...

0 Bolin Ding, et al. ∙

research

∙ 12/05/2017

Collecting Telemetry Data Privately

The collection and analysis of telemetry data from users' devices is rou...

0 Bolin Ding, et al. ∙

research

∙ 04/16/2017

Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm

The rise of robotic applications has led to the generation of a huge vol...

0 Shaoshan Liu, et al. ∙

Bolin Ding

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