Jiawei Jiang

According to our database, Jiawei Jiang authored at least 61 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Tackling Multiplayer Interaction for Federated Generative Adversarial Networks.

Chuang Hu, Tianyu Tu, Yili Gong, Jiawei Jiang, Zhigao Zheng, Dazhao Cheng

IEEE Trans. Mob. Comput., December, 2024

Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

VLDB J., September, 2024

ProjPert: Projection-Based Perturbation for Label Protection in Split Learning Based Vertical Federated Learning.

Fangcheng Fu, Xuanyu Wang, Jiawei Jiang, Huanran Xue, Bin Cui

IEEE Trans. Knowl. Data Eng., July, 2024

How good are machine learning clouds? Benchmarking two snapshots over 5 years.

Jiawei Jiang, Yi Wei, Yu Liu, Wentao Wu, Chuang Hu, Zhigao Zheng, Ziyi Zhang, Yingxia Shao, Ce Zhang

VLDB J., May, 2024

A systematic evaluation of machine learning on serverless infrastructure.

Jiawei Jiang, Shaoduo Gan, Bo Du, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Sheng Wang, Ce Zhang

VLDB J., 2024

Retrofitting Temporal Graph Neural Networks with Transformer.

Qiang Huang, Xiao Yan, Xin Wang, Susie Xi Rao, Zhichao Han, Fangcheng Fu, Wentao Zhang, Jiawei Jiang

CoRR, 2024

Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph.

Yuxiang Wang, Xiao Yan, Shiyu Jin, Quanqing Xu, Chuanhui Yang, Yuanyuan Zhu, Chuang Hu, Bo Du, Jiawei Jiang

CoRR, 2024

Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System.

Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu

CoRR, 2024

PICO: Accelerating All k-Core Paradigms on GPU.

Chen Zhao, Ting Yu, Zhigao Zheng, Song Jin, Jiawei Jiang, Bo Du, Dacheng Tao

CoRR, 2024

Self-Supervised Learning for Graph Dataset Condensation.

Yuxiang Wang, Xiao Yan, Shiyu Jin, Hao Huang, Quanqing Xu, Qingchen Zhang, Bo Du, Jiawei Jiang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning.

Yuxiang Wang, Xiao Yan, Chuang Hu, Quanqing Xu, Chuanhui Yang, Fangcheng Fu, Wentao Zhang, Hao Wang, Bo Du, Jiawei Jiang

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

HGAMLP: Heterogeneous Graph Attention MLP with De-Redundancy Mechanism.

Yuxuan Liang, Wentao Zhang, Zeang Sheng, Ling Yang, Jiawei Jiang, Yunhai Tong, Bin Cui

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Benchtemp: A General Benchmark for Evaluating Temporal Graph Neural Networks.

Qiang Huang, Xin Wang, Susie Xi Rao, Zhichao Han, Zitao Zhang, Yongjun He, Quanqing Xu, Yang Zhao, Zhigao Zheng, Jiawei Jiang

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

VFDV-IM: An Efficient and Securely Vertical Federated Data Valuation.

Xiaokai Zhou, Xiao Yan, Xinyan Li, Hao Huang, Quanqing Xu, Qinbo Zhang, Yen Jerome, Zhaohui Cai, Jiawei Jiang

Proceedings of the Database Systems for Advanced Applications, 2024

TreeCSS: An Efficient Framework for Vertical Federated Learning.

Qinbo Zhang, Xiao Yan, Yukai Ding, Quanqing Xu, Chuang Hu, Xiaokai Zhou, Jiawei Jiang

Proceedings of the Database Systems for Advanced Applications, 2024

Learning Diffusions under Uncertainty.

Hao Huang, Qian Yan, Keqi Han, Ting Gan, Jiawei Jiang, Quanqing Xu, Chuanhui Yang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System.

Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu

Proceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques, 2024

2023

Lasagne: A Multi-Layer Graph Convolutional Network Framework via Node-Aware Deep Architecture.

Xupeng Miao, Wentao Zhang, Yingxia Shao, Bin Cui, Lei Chen, Ce Zhang, Jiawei Jiang

IEEE Trans. Knowl. Data Eng., 2023

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning.

Yuxiang Wang, Xiao Yan, Chuang Hu, Fangcheng Fu, Wentao Zhang, Hao Wang, Shuo Shang, Jiawei Jiang

CoRR, 2023

BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks.

Qiang Huang, Jiawei Jiang, Susie Xi Rao, Ce Zhang, Zhichao Han, Zitao Zhang, Xin Wang, Yongjun He, Quanqing Xu, Yang Zhao, Chuang Hu, Shuo Shang, Bo Du

CoRR, 2023

2022

CuWide: Towards Efficient Flow-Based Training for Sparse Wide Models on GPUs.

Xupeng Miao, Lingxiao Ma, Zhi Yang, Yingxia Shao, Bin Cui, Lele Yu, Jiawei Jiang

IEEE Trans. Knowl. Data Eng., 2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update.

Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

Proc. VLDB Endow., 2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates.

Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

CoRR, 2022

Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

CoRR, 2022

In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?

Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Analyzing Online Transaction Networks with Network Motifs.

Jiawei Jiang, Yusong Hu, Xiaosen Li, Wen Ouyang, Zhitao Wang, Fangcheng Fu, Bin Cui

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Lasagne: A Multi-Layer Graph Convolutional Network Framework via Node-aware Deep Architecture (Extended Abstract).

Xupeng Miao, Wentao Zhang, Yingxia Shao, Bin Cui, Lei Chen, Ce Zhang, Jiawei Jiang

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

BRIGHT - Graph Neural Networks in Real-time Fraud Detection.

Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Distributed Machine Learning and Gradient Optimization

Jiawei Jiang, Bin Cui, Ce Zhang

Springer, ISBN: 978-981-16-3419-2, 2022

2021

Model averaging in distributed machine learning: a case study with Apache Spark.

Yunyan Guo, Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Bin Cui, Jianzhong Li

VLDB J., 2021

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition.

Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Yaliang Li, Bolin Ding, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui

Proc. VLDB Endow., 2021

BAGUA: Scaling up Distributed Learning with System Relaxations.

Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang

Proc. VLDB Endow., 2021

Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce.

Xupeng Miao, Xiaonan Nie, Yingxia Shao, Zhi Yang, Jiawei Jiang, Lingxiao Ma, Bin Cui

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Towards Demystifying Serverless Machine Learning Training.

Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

VF²Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning.

Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, Bin Cui

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

OpenBox: A Generalized Black-box Optimization Service.

Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract).

Xupeng Miao, Lingxiao Ma, Zhi Yang, Yingxia Shao, Bin Cui, Lele Yu, Jiawei Jiang

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Ease.ML: A Lifecycle Management System for Machine Learning.

Leonel Aguilar Melgar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Xi Rao, Johannes Rausch, Cédric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements.

Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

SKCompress: compressing sparse and nonuniform gradient in distributed machine learning.

Jiawei Jiang, Fangcheng Fu, Tong Yang, Yingxia Shao, Bin Cui

VLDB J., 2020

Snapshot boosting: a fast ensemble framework for deep neural networks.

Wentao Zhang, Jiawei Jiang, Yingxia Shao, Bin Cui

Sci. China Inf. Sci., 2020

Reliable Data Distillation on Graph Convolutional Network.

Wentao Zhang, Xupeng Miao, Yingxia Shao, Jiawei Jiang, Lei Chen, Olivier Ruas, Bin Cui

Proceedings of the 2020 International Conference on Management of Data, 2020

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript.

Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient Diversity-Driven Ensemble for Deep Neural Networks.

Wentao Zhang, Jiawei Jiang, Yingxia Shao, Bin Cui

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent.

Zhipeng Zhang, Wentao Wu, Jiawei Jiang, Lele Yu, Bin Cui, Ce Zhang

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

PSGraph: How Tencent trains extremely large-scale graphs with Spark?

Jiawei Jiang, Pin Xiao, Lele Yu, Xiaosen Li, Jiefeng Cheng, Xupeng Miao, Zhipeng Zhang, Bin Cui

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

Efficient Automatic CASH via Rising Bandits.

Yang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, Bin Cui

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

An Experimental Evaluation of Large Scale GBDT Systems.

Fangcheng Fu, Jiawei Jiang, Yingxia Shao, Bin Cui

Proc. VLDB Endow., 2019

PS2: Parameter Server on Spark.

Zhipeng Zhang, Bin Cui, Yingxia Shao, Lele Yu, Jiawei Jiang, Xupeng Miao

Proceedings of the 2019 International Conference on Management of Data, 2019

MLlib*: Fast Training of GLMs Using Spark MLlib.

Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Lele Yu, Bin Cui

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

Sparse Gradient Compression for Distributed SGD.

Haobo Sun, Yingxia Shao, Jiawei Jiang, Bin Cui, Kai Lei, Yu Xu, Jiang Wang

Proceedings of the Database Systems for Advanced Applications, 2019

FeatureBand: A Feature Selection Method by Combining Early Stopping and Genetic Local Search.

Huanran Xue, Jiawei Jiang, Yingxia Shao, Bin Cui

Proceedings of the Web and Big Data - Third International Joint Conference, 2019

2018

SketchML: Accelerating Distributed Machine Learning with Data Sketches.

Jiawei Jiang, Fangcheng Fu, Tong Yang, Bin Cui

Proceedings of the 2018 International Conference on Management of Data, 2018

DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions.

Jiawei Jiang, Bin Cui, Ce Zhang, Fangcheng Fu

Proceedings of the 2018 International Conference on Management of Data, 2018

2017

GVoS: A General System for Near-Duplicate Video-Related Applications on Storm.

Jiawei Jiang, Yunhai Tong, Hua Lu, Bin Cui, Kai Lei, Lele Yu

ACM Trans. Inf. Syst., 2017

Heterogeneity-aware Distributed Parameter Servers.

Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

TencentBoost: A Gradient Boosting Tree System with Parameter Server.

Jie Jiang, Jiawei Jiang, Bin Cui, Ce Zhang

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

StroMAX: Partitioning-Based Scheduler for Real-Time Stream Processing System.

Jiawei Jiang, Zhipeng Zhang, Bin Cui, Yunhai Tong, Ning Xu

Proceedings of the Database Systems for Advanced Applications, 2017

TeslaML: Steering Machine Learning Automatically in Tencent.

Jiawei Jiang, Ming Huang, Jie Jiang, Bin Cui

Proceedings of the Web and Big Data - First International Joint Conference, 2017

2013

A predictive dynamic load balancing algorithm with service differentiation.

Jiawei Jiang, Haojiang Deng, Xue Liu

Proceedings of the 15th IEEE International Conference on Communication Technology, 2013