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Improving Graph Representation Learning by Contrastive Regularization


Jan 27, 2021
Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng


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The item selection problem for user cold-start recommendation


Oct 27, 2020
Yitong Meng, Jie Liu, Xiao Yan, James Cheng


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Rethinking Graph Regularization For Graph Neural Networks


Sep 04, 2020
Han Yang, Kaili Ma, James Cheng


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Understanding Graph Neural Networks from Graph Signal Denoising Perspectives


Jun 08, 2020
Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng

* 19 pages, 8 figures 

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Boosting First-order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates


May 25, 2020
Kaiwen Zhou, Anthony Man-Cho So, James Cheng

* 27 pages, 7 figures 

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TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism


Apr 16, 2020
Zhenkun Cai, Kaihao Ma, Xiao Yan, Yidi Wu, Yuzhen Huang, James Cheng, Teng Su, Fan Yu


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Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs


Mar 16, 2020
Han Yang, Xiao Yan, Xinyan Dai, James Cheng


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Edit Distance Embedding using Convolutional Neural Networks


Jan 31, 2020
Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng


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Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning


Nov 25, 2019
Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin K. W. Ng, James Cheng, Yu Fan


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Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search


Nov 20, 2019
Xinyan Dai, Xiao Yan, Kelvin K. W. Ng, Jie Liu, James Cheng


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Understanding and Improving Proximity Graph based Maximum Inner Product Search


Sep 30, 2019
Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang

* 8 pages, 8 figures 

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Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)


Nov 05, 2018
Xiao Yan, Xinyan Dai, Jie Liu, Kaiwen Zhou, James Cheng


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VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning


Oct 28, 2018
Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao

* 46 pages, 25 figures. IEEE Transactions on Knowledge and Data Engineering, accepted in October, 2018. arXiv admin note: substantial text overlap with arXiv:1704.04966 

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Norm-Ranging LSH for Maximum Inner Product Search


Oct 22, 2018
Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng

* NIPS2018 

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Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications


Oct 11, 2018
Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin

* IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(9): 2066-2080, 2018 
* 29 pages, 19 figures 

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ASVRG: Accelerated Proximal SVRG


Oct 07, 2018
Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin

* 32 pages, 3 figures 

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A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates


Jun 28, 2018
Kaiwen Zhou, Fanhua Shang, James Cheng

* ICML2018 

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Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization


Feb 28, 2018
Fanhua Shang, Yuanyuan Liu, James Cheng

* 26 pages, 7 figures, AISTATS 2016. arXiv admin note: text overlap with arXiv:1606.01245 

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Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization


Feb 26, 2018
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K. W. Ng, Yuichi Yoshida

* 24 pages, 10 figures, AISTATS 2018. arXiv admin note: text overlap with arXiv:1703.06807 

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Accelerated Variance Reduced Stochastic ADMM


Jul 11, 2017
Yuanyuan Liu, Fanhua Shang, James Cheng

* 16 pages, 5 figures, Appears in Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, USA, pp. 2287--2293, 2017 

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Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent


Jun 04, 2017
Fanhua Shang, Yuanyuan Liu, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida

* 25 pages, 8 figures 

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Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning


Apr 17, 2017
Fanhua Shang, Yuanyuan Liu, James Cheng, Jiacheng Zhuo

* Corrected a few typos in this version 

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Unified Scalable Equivalent Formulations for Schatten Quasi-Norms


Nov 27, 2016
Fanhua Shang, Yuanyuan Liu, James Cheng

* 21 pages. CUHK Technical Report CSE-ShangLC20160307, March 7, 2016 

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Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization


Jun 04, 2016
Fanhua Shang, Yuanyuan Liu, James Cheng

* 16 pages, 5 figures, Appears in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona, USA, pp. 2016--2022, 2016 

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Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning


Jan 16, 2016
Fanhua Shang, James Cheng, Hong Cheng

* 18 pages, 10 figures 

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Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations


Sep 03, 2014
Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng

* 28 pages, 9 figures 

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Generalized Higher-Order Tensor Decomposition via Parallel ADMM


Jul 05, 2014
Fanhua Shang, Yuanyuan Liu, James Cheng

* 9 pages, 5 figures, AAAI 2014 

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