Details of Research Outputs

TitleScalable Gaussian Process Using Inexact Admm for Big Data
Author (Name in English or Pinyin)
Xu, Y.1,2; Yin, F.2; Zhang, J.2; Xu, W.1; Cui, S.1,3; Luo, Z.-Q.2
Date Issued2019-05-12
Conference NameICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source PublicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference PlaceBrighton, United Kingdom
DOI10.1109/ICASSP.2019.8682350
Indexed BySCOPUS
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages7495-7499
References
[1] C. E. Rasmussen and C. I. K. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.
[2] J. Snoek, H. Larochelle, and R. P. Adams, "Practical Bayesian optimization of machine learning algorithms, " in Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA, December 2012, pp. 2951-2959.
[3] C. K. Williams and M. Seeger, "Using the Nyström method to speed up kernel machines, " in Advances in Neural Information Processing Systems (NIPS), Denver, CO, USA, December 2000, pp. 682-688.
[4] S. Ambikasaran, D. Foreman-Mackey, L. Greengard, D. W. Hogg, and M. O'Neil, "Fast direct methods for Gaussian processes, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 252-265, February 2016.
[5] S. Sarkka, A. Solin, and J. Hartikainen, "Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering, " IEEE Signal Process. Mag., vol. 30, no. 4, pp. 51-61, July 2013.
[6] V. Tresp, "A Bayesian committee machine, " Neural Computation, vol. 30, no. 12, pp. 2719-2741, November 2000.
[7] M. P. Deisenroth and J. W. Ng, "Distributed Gaussian processes, " in International Conference on Machine Learning (ICML), Lille, France, July 2015, pp. 1481-1490.
[8] J. Quiñonero Candela and C. E. Rasmussen, "A unifying view of sparse approximate Gaussian process regression, " J. Mach. Learn. Res., vol. 6, no. 1, pp. 1939-1959, December 2005.
[9] M. K. Titsias, "Variational learning of inducing variables in sparse Gaussian processes, " in International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA, April 2009, pp. 567-574.
[10] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers, " Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, January 2011.
[11] Y. Xu, W. Xu, F. Yin, J. Lin, and S. Cui, "High-accuracy wireless traffic prediction: A GP-based machine learning approach, " in IEEE Global Communications Conference (GLOBECOM), Singapore, December 2017, pp. 1-6.
[12] F. Yin, X. He, L. Pan, T. Chen, Z.-Q. Luo, and S. Theodoridis, "Sparse structure enabled grid spectral mixture kernel for temporal Gaussian process regression, " in International Conference on Information Fusion (FUSION), Cambridge UK, July 2018, pp. 47-54.
[13] T. Chen, "On kernel design for regularized LTI system identification, " Automatica, vol. 90, no. 1, pp. 109-122, April 2018.
[14] D. Garcia, "Robust smoothing of gridded data in one and higher dimensions with missing values, " Computational Statistics and Data Analysis, vol. 54, no. 4, pp. 1167-1178, September 2010.
[15] M. Hong, Z. Q. Luo, and M. Razaviyayn, "Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems, " SIAM Journal on Optimization, vol. 26, no. 1, pp. 337-364, January 2016.
[16] G. H. Golub and C. F. Van Loan, Matrix Computations, The Johns Hopkins University Press, 2013.
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/900
CollectionSchool of Science and Engineering
Co-First AuthorYin, F.
Affiliation
1.Key Lab of Universal Wireless Communications, Beijing University of Posts and Telecommunications, China
2.Chinese University of Hong Kong, Shenzhen, China
3.Department of Electrical and Computer Engineering, University of California, Davis, United States
Recommended Citation
GB/T 7714
Xu, Y.,Yin, F.,Zhang, J.et al. Scalable Gaussian Process Using Inexact Admm for Big Data[C],2019.
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