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2020 – today
- 2023
[j48]Barakeel Fanseu Kamhoua
, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han:
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. ACM Trans. Knowl. Discov. Data 17(3): 31:1-31:31 (2023)- [c96]Zhenkun Cai, Qihui Zhou, Xiao Yan, Da Zheng, Xiang Song, Chenguang Zheng, James Cheng, George Karypis:
DSP: Efficient GNN Training with Multiple GPUs. PPoPP 2023: 392-404 - [i57]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR abs/2304.11327 (2023) - 2022
- [j47]Chenguang Zheng, Hongzhi Chen, Yuxuan Cheng, Zhezheng Song, Yifan Wu, Changji Li, James Cheng, Hao Yang, Shuai Zhang:
ByteGNN: Efficient Graph Neural Network Training at Large Scale. Proc. VLDB Endow. 15(6): 1228-1242 (2022) - [j46]Hongzhi Chen, Changji Li, Chenguang Zheng, Chenghuan Huang, Juncheng Fang, James Cheng, Jie Zhang:
G-Tran: A High Performance Distributed Graph Database with a Decentralized Architecture. Proc. VLDB Endow. 15(11): 2545-2558 (2022) - [j45]Changji Li, Hongzhi Chen, Shuai Zhang, Yingqian Hu, Chao Chen, Zhenjie Zhang, Meng Li, Xiangchen Li, Dongqing Han, Xiaohui Chen, Xudong Wang, Huiming Zhu, Xuwei Fu, Tingwei Wu, Hongfei Tan, Hengtian Ding, Mengjin Liu, Kangcheng Wang, Ting Ye, Lei Li, Xin Li, Yu Wang, Chenguang Zheng, Hao Yang, James Cheng:
ByteGraph: A High-Performance Distributed Graph Database in ByteDance. Proc. VLDB Endow. 15(12): 3306-3318 (2022) - [j44]Pengfei Zuo, Qihui Zhou, Jiazhao Sun, Liu Yang, Shuangwu Zhang, Yu Hua, James Cheng, Rongfeng He, Huabing Yan:
RACE: One-sided RDMA-conscious Extendible Hashing. ACM Trans. Storage 18(2): 11:1-11:29 (2022) - [j43]Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, Zhenkun Cai, Yuzhen Huang, James Cheng, Han Yuan, Fan Yu:
Elastic Deep Learning in Multi-Tenant GPU Clusters. IEEE Trans. Parallel Distributed Syst. 33(1): 144-158 (2022) - [j42]Zhenkun Cai, Xiao Yan, Kaihao Ma, Yidi Wu, Yuzhen Huang, James Cheng, Teng Su, Fan Yu:
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism. IEEE Trans. Parallel Distributed Syst. 33(8): 1967-1981 (2022) - [c95]Kaiwen Zhou, Lai Tian, Anthony Man-Cho So, James Cheng:
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums. AISTATS 2022: 3684-3708 - [c94]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c93]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. ICML 2022: 7144-7163 - [c92]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c91]Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng:
Exact Shape Correspondence via 2D graph convolution. NeurIPS 2022 - [c90]Guanxian Jiang, Qihui Zhou, Tatiana Jin, Boyang Li, Yunjian Zhao, Yichao Li, James Cheng:
VSGM: View-Based GPU-Accelerated Subgraph Matching on Large Graphs. SC 2022: 52:1-52:15 - [c89]Yuntao Gui, Yidi Wu, Han Yang, Tatiana Jin, Boyang Li, Qihui Zhou, James Cheng, Fan Yu:
HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization. SC 2022: 72:1-72:15 - [i56]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i55]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i54]Binghui Xie, Chenhan Jin, Kaiwen Zhou, James Cheng, Wei Meng:
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms. CoRR abs/2205.02273 (2022) - [i53]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR abs/2206.07314 (2022) - [i52]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng:
Pareto Invariant Risk Minimization. CoRR abs/2206.07766 (2022) - [i51]Chenhan Jin, Kaiwen Zhou, Bo Han, James Cheng, Ming-Chang Yang:
Efficient Private SCO for Heavy-Tailed Data via Clipping. CoRR abs/2206.13011 (2022) - [i50]Yifan Hou, Jie Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang:
Measuring and Improving the Use of Graph Information in Graph Neural Networks. CoRR abs/2206.13170 (2022) - [i49]Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang:
A Representation Learning Framework for Property Graphs. CoRR abs/2206.13176 (2022) - [i48]Peiqi Yin, Xiao Yan, Jinjing Zhou, Qiang Fu, Zhenkun Cai, James Cheng, Bo Tang, Minjie Wang:
DGI: Easy and Efficient Inference for GNNs. CoRR abs/2211.15082 (2022) - 2021
- [j41]Guimu Guo, Hongzhi Chen, Da Yan, James Cheng, Jake Y. Chen, Zechen Chong:
Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System. IEEE ACM Trans. Comput. Biol. Bioinform. 18(2): 731-744 (2021) - [j40]Yunjian Zhao, Zhi Liu, Yidi Wu, Guanxian Jiang, James Cheng, Kunlong Liu, Xiao Yan:
Timestamped State Sharing for Stream Analytics. IEEE Trans. Parallel Distributed Syst. 32(11): 2691-2704 (2021) - [c88]Han Yang, Kaili Ma, James Cheng:
Rethinking Graph Regularization for Graph Neural Networks. AAAI 2021: 4573-4581 - [c87]Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han:
HyperGraph Convolution Based Attributed HyperGraph Clustering. CIKM 2021: 453-463 - [c86]Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, Fan Yu:
DGCL: an efficient communication library for distributed GNN training. EuroSys 2021: 130-144 - [c85]Yidi Wu, Kaihao Ma, Zhenkun Cai, Tatiana Jin, Boyang Li, Chengguang Zheng, James Cheng, Fan Yu:
Seastar: vertex-centric programming for graph neural networks. EuroSys 2021: 359-375 - [c84]Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng:
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. IJCNN 2021: 1-8 - [c83]Yidi Wu, Yuntao Gui, Tatiana Jin, James Cheng, Xiao Yan, Peiqi Yin, Yufei Cai, Bo Tang, Fan Yu:
Vertex-Centric Visual Programming for Graph Neural Networks. SIGMOD Conference 2021: 2803-2807 - [c82]Yihui Feng, Zhi Liu, Yunjian Zhao, Tatiana Jin, Yidi Wu, Yang Zhang, James Cheng, Chao Li, Tao Guan:
Scaling Large Production Clusters with Partitioned Synchronization. USENIX Annual Technical Conference 2021: 81-97 - [i47]Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng:
Improving Graph Representation Learning by Contrastive Regularization. CoRR abs/2101.11525 (2021) - [i46]Hongzhi Chen, Changji Li, Chenguang Zheng, Chenghuan Huang, Juncheng Fang, James Cheng, Jie Zhang:
G-Tran: Making Distributed Graph Transactions Fast. CoRR abs/2105.04449 (2021) - [i45]Kaiwen Zhou, Lai Tian, Anthony Man-Cho So, James Cheng:
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums. CoRR abs/2105.12062 (2021) - [i44]Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng:
Local Reweighting for Adversarial Training. CoRR abs/2106.15776 (2021) - [i43]Kaiwen Zhou, Anthony Man-Cho So, James Cheng:
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization. CoRR abs/2109.15292 (2021) - 2020
- [j39]James Cheng, Starr Hoffman:
Librarians and Administrators on Academic Library Impact Research: Characteristics and Perspectives. Coll. Res. Libr. 81(3) (2020) - [j38]Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao:
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning. IEEE Trans. Knowl. Data Eng. 32(1): 188-202 (2020) - [c81]Xinyan Dai, Xiao Yan, Kelvin Kai Wing Ng, Jiu Liu, James Cheng:
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search. AAAI 2020: 51-58 - [c80]Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang:
Understanding and Improving Proximity Graph Based Maximum Inner Product Search. AAAI 2020: 139-146 - [c79]Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Jun Guo, Benben Liao, Guangyong Chen:
PMD: An Optimal Transportation-Based User Distance for Recommender Systems. ECIR (2) 2020: 272-280 - [c78]Tatiana Jin, Zhenkun Cai, Boyang Li, Chengguang Zheng, Guanxian Jiang, James Cheng:
Improving resource utilization by timely fine-grained scheduling. EuroSys 2020: 20:1-20:16 - [c77]Yifan Hou, Jie Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang:
Measuring and Improving the Use of Graph Information in Graph Neural Networks. ICLR 2020 - [c76]Qinghua Ding, Kaiwen Zhou, James Cheng:
Tight Convergence Rate of Gradient Descent for Eigenvalue Computation. IJCAI 2020: 3276-3282 - [c75]Kaiwen Zhou, Anthony Man-Cho So, James Cheng:
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates. NeurIPS 2020 - [c74]Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng:
Convolutional Embedding for Edit Distance. SIGIR 2020: 599-608 - [c73]Hongzhi Chen, Bowen Wu, Shiyuan Deng, Chenghuan Huang, Changji Li, Yichao Li, James Cheng:
High Performance Distributed OLAP on Property Graphs with Grasper. SIGMOD Conference 2020: 2705-2708 - [c72]Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng:
Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning. UAI 2020: 211-220 - [c71]Yitong Meng, Xiao Yan, Weiwen Liu, Huanhuan Wu, James Cheng:
Wasserstein Collaborative Filtering for Item Cold-start Recommendation. UMAP 2020: 318-322 - [i42]Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng:
Edit Distance Embedding using Convolutional Neural Networks. CoRR abs/2001.11692 (2020) - [i41]Han Yang, Xiao Yan, Xinyan Dai, James Cheng:
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs. CoRR abs/2002.07518 (2020) - [i40]Zhenkun Cai, Kaihao Ma, Xiao Yan, Yidi Wu, Yuzhen Huang, James Cheng, Teng Su, Fan Yu:
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism. CoRR abs/2004.10856 (2020) - [i39]Kaiwen Zhou, Anthony Man-Cho So, James Cheng:
Boosting First-order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates. CoRR abs/2005.12061 (2020) - [i38]Guoji Fu, Yifan Hou, Jie Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng:
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives. CoRR abs/2006.04386 (2020) - [i37]Haibo Xiu, Xiao Yan, Xiaoqiang Wang, James Cheng, Lei Cao:
Hierarchical Graph Matching Network for Graph Similarity Computation. CoRR abs/2006.16551 (2020) - [i36]Han Yang, Kaili Ma, James Cheng:
Rethinking Graph Regularization For Graph Neural Networks. CoRR abs/2009.02027 (2020) - [i35]Yitong Meng, Jie Liu, Xiao Yan, James Cheng:
The item selection problem for user cold-start recommendation. CoRR abs/2010.14013 (2020)
2010 – 2019
- 2019
- [j37]Yuzhen Huang, Yingjie Shi, Zheng Zhong, Yihui Feng, James Cheng, Jiwei Li, Haochuan Fan, Chao Li, Tao Guan, Jingren Zhou:
Yugong: Geo-Distributed Data and Job Placement at Scale. Proc. VLDB Endow. 12(12): 2155-2169 (2019) - [c70]Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo:
Direct Acceleration of SAGA using Sampled Negative Momentum. AISTATS 2019: 1602-1610 - [c69]Shiyuan Deng, Xiao Yan, Kelvin Kai Wing Ng, Chenyu Jiang, James Cheng:
Pyramid: A General Framework for Distributed Similarity Search on Large-scale Datasets. IEEE BigData 2019: 1066-1071 - [c68]Hongzhi Chen, Changji Li, Juncheng Fang, Chenghuan Huang, James Cheng, Jie Zhang, Yifan Hou, Xiao Yan:
Grasper: A High Performance Distributed System for OLAP on Property Graphs. SoCC 2019: 87-100 - [c67]Da Yan, James Cheng, Hongzhi Chen, Cheng Long, Purushotham V. Bangalore:
Lightweight Fault Tolerance in Pregel-Like Systems. ICPP 2019: 69:1-69:10 - [c66]Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang:
A Representation Learning Framework for Property Graphs. KDD 2019: 65-73 - [c65]Hongzhi Chen, Xiaoxi Wang, Chenghuan Huang, Juncheng Fang, Yifan Hou, Changji Li, James Cheng:
Large Scale Graph Mining with G-Miner. SIGMOD Conference 2019: 1881-1884 - [c64]Yuzhen Huang, Xiao Yan, Guanxian Jiang, Tatiana Jin, James Cheng, An Xu, Zhanhao Liu, Shuo Tu:
Tangram: Bridging Immutable and Mutable Abstractions for Distributed Data Analytics. USENIX Annual Technical Conference 2019: 191-206 - [i34]Shiyuan Deng, Xiao Yan, Kelvin Kai Wing Ng, Chenyu Jiang, James Cheng:
Pyramid: A General Framework for Distributed Similarity Search. CoRR abs/1906.10602 (2019) - [i33]Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, James Cheng:
Elastic deep learning in multi-tenant GPU cluster. CoRR abs/1909.11985 (2019) - [i32]Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang:
Understanding and Improving Proximity Graph based Maximum Inner Product Search. CoRR abs/1909.13459 (2019) - [i31]Xinyan Dai, Xiao Yan, Kelvin Kai Wing Ng, Jie Liu, James Cheng:
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search. CoRR abs/1911.04654 (2019) - [i30]Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin Kai Wing Ng, James Cheng, Yu Fan:
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning. CoRR abs/1911.04655 (2019) - 2018
- [j36]Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin:
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. IEEE Trans. Pattern Anal. Mach. Intell. 40(9): 2066-2080 (2018) - [j35]Yuzhen Huang, Tatiana Jin, Yidi Wu, Zhenkun Cai, Xiao Yan, Fan Yang, Jinfeng Li, Yuying Guo, James Cheng:
FlexPS: Flexible Parallelism Control in Parameter Server Architecture. Proc. VLDB Endow. 11(5): 566-579 (2018) - [j34]Fanhua Shang, Yuanyuan Liu, James Cheng, Da Yan:
Fuzzy Double Trace Norm Minimization for Recommendation Systems. IEEE Trans. Fuzzy Syst. 26(4): 2039-2049 (2018) - [j33]Da Yan, Yuzhen Huang, Miao Liu, Hongzhi Chen, James Cheng, Huanhuan Wu, Chengcui Zhang:
GraphD: Distributed Vertex-Centric Graph Processing Beyond the Memory Limit. IEEE Trans. Parallel Distributed Syst. 29(1): 99-114 (2018) - [c63]Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin:
ASVRG: Accelerated Proximal SVRG. ACML 2018: 815-830 - [c62]Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. AISTATS 2018: 1027-1036 - [c61]Hongzhi Chen, Miao Liu, Yunjian Zhao, Xiao Yan, Da Yan, James Cheng:
G-Miner: an efficient task-oriented graph mining system. EuroSys 2018: 32:1-32:12 - [c60]Da Yan, Hongzhi Chen, James Cheng, Zhenkun Cai, Bin Shao:
Scalable De Novo Genome Assembly Using Pregel. ICDE 2018: 1216-1219 - [c59]Kaiwen Zhou, Fanhua Shang, James Cheng:
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. ICML 2018: 5975-5984 - [c58]Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng:
Norm-Ranging LSH for Maximum Inner Product Search. NeurIPS 2018: 2956-2965 - [c57]Jinfeng Li, Xiao Yan, Jie Zhang, An Xu, James Cheng, Jie Liu, Kelvin Kai Wing Ng, Ti-Chung Cheng:
A General and Efficient Querying Method for Learning to Hash. SIGMOD Conference 2018: 1333-1347 - [i29]Da Yan, Hongzhi Chen, James Cheng, Zhenkun Cai, Bin Shao:
Scalable De Novo Genome Assembly Using Pregel. CoRR abs/1801.04453 (2018) - [i28]Fanhua Shang, Kaiwen Zhou, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao:
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning. CoRR abs/1802.09932 (2018) - [i27]Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. CoRR abs/1802.09933 (2018) - [i26]Fanhua Shang, Yuanyuan Liu, James Cheng:
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. CoRR abs/1803.00420 (2018) - [i25]Kaiwen Zhou, Fanhua Shang, James Cheng:
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. CoRR abs/1806.11027 (2018) - [i24]Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng:
Norm-Ranging LSH for Maximum Inner Product Search. CoRR abs/1809.08782 (2018) - [i23]Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin:
ASVRG: Accelerated Proximal SVRG. CoRR abs/1810.03105 (2018) - [i22]Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin:
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. CoRR abs/1810.05186 (2018) - [i21]Xiao Yan, Xinyan Dai, Jie Liu, Kaiwen Zhou, James Cheng:
Norm-Range Partition: A Univiseral Catalyst for LSH based Maximum Inner Product Search (MIPS). CoRR abs/1810.09104 (2018) - 2017
- [b1]Da Yan, Yuanyuan Tian, James Cheng:
Systems for Big Graph Analytics. Springer Briefs in Computer Science, Springer 2017, ISBN 978-3-319-58216-0, pp. 1-92 - [j32]Zhiqiang Xu, James Cheng, Xiaokui Xiao, Ryohei Fujimaki, Yusuke Muraoka:
Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering. Knowl. Inf. Syst. 53(1): 239-268 (2017) - [j31]Fan Yang, Fanhua Shang, Yuzhen Huang, James Cheng, Jinfeng Li, Yunjian Zhao, Ruihao Zhao:
LFTF: A Framework for Efficient Tensor Analytics at Scale. Proc. VLDB Endow. 10(7): 745-756 (2017) - [c56]Yuanyuan Liu, Fanhua Shang, James Cheng:
Accelerated Variance Reduced Stochastic ADMM. AAAI 2017: 2287-2293 - [c55]Qizhen Zhang, Hongzhi Chen, Da Yan, James Cheng, Boon Thau Loo, Purushotham V. Bangalore:
Architectural implications on the performance and cost of graph analytics systems. SoCC 2017: 40-51 - [c54]Huanhuan Wu, Yunjian Zhao, James Cheng, Da Yan:
Efficient Processing of Growing Temporal Graphs. DASFAA (2) 2017: 387-403 - [c53]Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao:
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. NIPS 2017: 4868-4877 - [c52]Jinfeng Li, James Cheng, Fan Yang, Yuzhen Huang, Yunjian Zhao, Xiao Yan, Ruihao Zhao:
LoSHa: A General Framework for Scalable Locality Sensitive Hashing. SIGIR 2017: 635-644 - [c51]Fan Yang, Yuzhen Huang, Yunjian Zhao, Jinfeng Li, Guanxian Jiang, James Cheng:
The Best of Both Worlds: Big Data Programming with Both Productivity and Performance. SIGMOD Conference 2017: 1619-1622 - [i20]Fanhua Shang, Yuanyuan Liu, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Variance Reduced Stochastic Gradient Descent with Sufficient Decrease. CoRR abs/1703.06807 (2017) - [i19]Fanhua Shang, Yuanyuan Liu, James Cheng, Jiacheng Zhuo:
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning. CoRR abs/1703.07948 (2017) - [i18]Yuanyuan Liu, Fanhua Shang, James Cheng:
Accelerated Variance Reduced Stochastic ADMM. CoRR abs/1707.03190 (2017) - [i17]Da Yan, Hongzhi Chen, James Cheng, M. Tamer Özsu, Qizhen Zhang, John C. S. Lui:
G-thinker: Big Graph Mining Made Easier and Faster. CoRR abs/1709.03110 (2017) - 2016
- [j30]Fan Yang, Jinfeng Li, James Cheng:
Husky: Towards a More Efficient and Expressive Distributed Computing Framework. Proc. VLDB Endow. 9(5): 420-431 (2016) - [j29]Da Yan, James Cheng, M. Tamer Özsu, Fan Yang, Yi Lu, John C. S. Lui, Qizhen Zhang, Wilfred Ng:
A General-Purpose Query-Centric Framework for Querying Big Graphs. Proc. VLDB Endow. 9(7): 564-575 (2016) - [j28]Huanhuan Wu, James Cheng, Yiping Ke, Silu Huang, Yuzhen Huang, Hejun Wu:
Efficient Algorithms for Temporal Path Computation. IEEE Trans. Knowl. Data Eng. 28(11): 2927-2942 (2016) - [j27]Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng:
Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition. IEEE Trans. Neural Networks Learn. Syst. 27(12): 2551-2563 (2016) - [j26]Yanyan Xu, James Cheng, Ada Wai-Chee Fu:
Distributed Maximal Clique Computation and Management. IEEE Trans. Serv. Comput. 9(1): 110-122 (2016) - [c50]Fanhua Shang, Yuanyuan Liu, James Cheng:
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. AAAI 2016: 2016-2022 - [c49]Fanhua Shang, Yuanyuan Liu, James Cheng:
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. AISTATS 2016: 620-629 - [c48]Cheng Chen, Hejun Wu, Dyce Jing Zhao, Da Yan, James Cheng:
SGraph: A Distributed Streaming System for Processing Big Graphs. BigCom 2016: 285-294 - [c47]