Publications

Summary: I have 15 accepted journal papers, 4 submitted journal papers, and 27 published conference papers.

Google Scholar Profile

Journal papers (submitted)

[19] Y. Shen, X. Fu, G. B. Giannakis, and N. D. Sidiropoulos, "Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices," IEEE Transactions on Signal and Information Processing over Networks, submitted July 2019.

[18] E. Ceci, Y. Shen, G. B. Giannakis, and S. Barbarossa, "Graph-based Learning under Perturbations via Total Least-Squares," IEEE Transactions on Signal Processing, submitted June 2019.

[17] Y. Shen, G. B. Giannakis, and B. Baingana, "Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity," IEEE Transactions on Signal Processing, submitted September 2018. [pdf]

[16] Y. Shen, P. A. Traganitis, and G. B. Giannakis, "Graph-Adaptive Nonlinear Dimensionality Reduction," IEEE Transactions on Signal Processing, submitted March 2018. [pdf]

Journal papers (published)

[15] Y. Shen, Geert Leus and G. B. Giannakis, “Online Graph-Adaptive Learning with Scalability and Privacy,” IEEE Transactions on Signal Processing, to appear May 2019. [pdf]

[14] V. N. Ioannidis, Y. Shen, and G. B. Giannakis, "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs," IEEE Transactions on Signal Processing, to appear May 2019. [pdf]

[13] Y. Shen, T. Chen, and G. B. Giannakis, "Random Feature-based Online Multi-kernel Learning in Non-stationary and Adversarial Environments," Journal of Machine Learning Research (JMLR), February 2019. [pdf][code]

[12] T. Chen, Q. Ling, Y. Shen, and G. B. Giannakis, "Heterogeneous Online Learning for “Thing-Adaptive” Low-Latency Fog Computing in IoT," IEEE Internet of Things Journal, December, 2018.[pdf]

[11] J. Chen, G. Wang, Y. Shen, and G. B. Giannakis, "Canonical Correlation Analysis of Datasets with a Common Source Graph," IEEE Transactions on Signal Processing, vol. 66, no. 16, pp. 4398-4408, August 2018. [pdf]

[10] G. B. Giannakis, Y. Shen, and G. V. Karanikolas, "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics," Proceedings of the IEEE, vol. 106, no. 5, pp. 787-807, May 2018. [pdf] (First student author)

[9] Y. Shen, M. Mardani, and G. B. Giannakis, "Online Categorical Subspace Learning for Sketching Big Data with Misses," IEEE Transactions on Signal Processing, vol. 65, no. 15, pp. 4004-4018, August 2017. [pdf]

[8] Y. Shen, B. Baingana, and G. B. Giannakis, "Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks," IEEE Transactions on Signal Processing, vol. 65, no. 14, pp. 3675 - 3687, July 2017.[pdf]

[7] Y. Shen, B. Baingana, and G. B. Giannakis, "Kernel-based structural equation models for topology identification of directed networks," IEEE Trans. on Sig. Processing, vol. 65, no. 10, pp. 2503-2516, May 2017.[pdf]

[6] J. Fang, F. Wang, Y. Shen, Hongbin Li and Rick S. Blum, "Super-Resolution Compressed Sensing for Line Spectral Estimation: An Iterative Reweighted Approach," IEEE Trans. on Signal Processing, vol. 64, no. 18, pp. 4649-4662, September 2016.[pdf]

[5] J. Fang, Y. Shen, L. Yang and H. Li, "Adaptive one-bit quantization for compressed sensing," Signal Processing (Elsevier), vol.125, pp.145-155, Aug. 2016. [pdf] (First student author)

[4] J. Fang, Y. Shen, H. Li and Pu Wang, ''Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals,'' IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 360-372, January 2015. [pdf] (First student author)

[3] J. Fang, Y. Shen, H. Li and Z. Ren, "Sparse signal recovery from one-bit quantized data: an iterative reweighted algorithm," Signal Processing (Elsevier), vol.102, pp.201-206, September 2014. [pdf] (First student author)

[2] J. Fang, J. Li, Y. Shen, H. Li, and S. Li, "Super-resolution compressed sensing: an iterative reweighted algorithm for joint parameter learning and sparse signal recovery," IEEE Signal Processing Letters, vol.21, no.6, pp.761-765, June 2014. [pdf]

[1] Y. Shen, J. Fang, and H. Li, "Exact reconstruction analysis of log-sum minimization for compressed sensing," IEEE Signal Processing Letters, vol.20, no.12, pp.1223-1226, December 2013. [pdf]


Conference papers

[27] S. Barrash, Y. Shen, and G. B. Giannakis, “Nonlinear kNN Learning over Graphs,” Proc. of CAMSAP Conf, Guadeloupe, West Indies, Dec. 15-18, 2019.

[26] Y. Shen, and Geert Leus, “Scalable Learning with Privacy over Graphs,” Proc. of Data Science Workshop, Minneapolis, MN, June 2-5, 2019.

[25] E. Ceci, Y. Shen, G. B. Giannakis, and S. Barbarossa, “Signal and Graph Perturbations via Total Least Squares,” Proc. of Asilomar Conf. on Sig., Systems, and Computers , Pacific Grove, CA, October 28-31, 2018.

[24] V. N. Ioannidis, Y. Shen, P. A. Traganitis, and G. B. Giannakis, “Kernel-Based Learning of Processes over Multi-layer Graphs,” Proc. of Intl. Workshop on Signal Process. Advances in Wireless Communications, Kalamata, Greece, June 25-28, 2018.

[23] J. Chen, G. Wang, Y. Shen, and G. B. Giannakis, ”Canonical Correlation Analysis on Graphs,” Proc. of Statistical Signal Processing Workshop, Freiburg, Germany, June 10-13, 2018.

[22] Y. Shen and G. B. Giannakis, “Online Nonlinear Sparse Structural Vector AR models for Identifying Dynamic Graph Topologies,” Proc. of Data Science Workshop, Lausanne, Switzerland, June 4-6, 2018.

[21] V. N. Ioannidis, Y. Shen, and G. B. Giannakis, ”Semi-blind Inference of Topologies and Signals over Graphs,” Proc. of Data Science Workshop, Lausanne, Switzerland, June 4-6, 2018.

[20] Y. Shen, T. Chen and G.B.Giannakis, “Online Multi-Kernel Learning with Orthogonal Random Features,” Proc. of Intl. Conf. on Acoust., Speech, and Signal Process., Calgary, Alberta, Canada, Apr. 15-20, 2018.

[19] Y. Shen , T. Chen and G. B. Giannakis, “Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments,” Proc. of Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Canary Islands, April 9-11, 2018.

[18] Y. Shen, P. A. Traganitis, and G. B. Giannakis, "Nonlinear Dimensionality Reduction on Graphs," Proc. of CAMSAP Conf., Curacao, Dutch Antilles, Dec. 10-13, 2017. (Best Student Paper Finalist)

[17] T. Chen Y. Shen, Q. Ling, and G. B. Giannakis, "Online Learning for "Thing-Adaptive" Fog Computing in IoT," Proc. of Asilomar Conf., Pacific Grove, CA, Oct. 29 - Nov. 1, 2017. (Best Student Paper Finalist)

[16] Y. Shen, X. Fu, G. B. Giannakis, and N. D. Sidiropoulos, "Inferring Directed Network Topologies via Joint Diagonalization," Proc. of Asilomar Conf., Pacific Grove, CA, Oct. 29 - Nov. 1, 2017.

[15] P. A. Traganitis, Y. Shen, and G. B. Giannakis, "Network Topology Inference via Elastic Net SEMs," Proc. of EUSIPCO, Kos Island, Greece, Aug. 28 - Sept. 3, 2017.

[14] P. A. Traganitis, Y. Shen, and G. B. Giannakis, "Topology Inference for Multilayer Networks," Proc. of Intl. Workshop on Network Science for Comms, May 2017. (ARO/ARL Travel grant Award)

[13] Y. Shen, B. Baingana, and G. B. Giannakis, "Topology Inference of Directed Graphs using Nonlinear Structural Vector Autoregressive Models," Proc. of Intl. Conf. on Acoust., Speech, and Signal Process., New Orleans, USA, Mar. 5-9, 2017.

[12] Y. Shen, B. Baingana, and G. B. Giannakis, "Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization," Proc. of Globalsip, Washington, DC, Dec. 7-9, 2016. (SPS Travel Grant Award)

[11] Y. Shen, B. Baingana, and G. B. Giannakis, "Inferring Directed Network Topologies via Tensor Factorization," Proc. of Asilomar Conf., Pacific Grove, CA, Nov. 6-9, 2016.

[10] Y. Shen and G. B. Giannakis, "Online Dictionary Learning for Large-Scale Binary Data," Proc. of EUSIPCO, Budapest, Hungary, Sept. 2016.

[9] Y. Shen, B. Baingana, and G. B. Giannakis, "Graph topology inference using nonlinear structural equation models," Graph Signal Processing Workshop, Philadelphia, PA, May 2016. (NSF Student Travel Grant)

[8] Y. Shen, B. Baingana, and G. B. Giannakis, "Nonlinear Structural Equation Models for Network Topology Inference,"Proc. of Conf. on Info. Sciences and Systems, Princeton, NJ, March 16-18, 2016.

[7] Y. Shen, M. Mardani, and G. B. Giannakis, "Online Sketching of Big Categorical Data with Absent Features," Proc. of Conf. on Info. Sciences and Systems, Johns Hopkins Univ., Baltimore, MD, March 18-20, 2015.

[6] J. Fang, Y. Shen, F. Li, H. Li, Z. Chen, "Support Knowledge-Aided Sparse Bayesian Learning for Compressed Sensing," in Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Brisbane, Australia, April 19-24, 2015.

[5] J. Fang, J. Li, Y. Shen, H. Li, and S. Li, "Super-resolution compressed sensing: an iterative reweighted algorithm for joint parameter learning and sparse signal recovery," in Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Brisbane, Australia, April 19-24, 2015.

[4] J. Fang, Y. Shen, H. Li, "Pattern coupled sparse Bayesian learning for recovery of time varying sparse signals," in Proceedings 2014 19th International Conference on Digital Signal Processing , Hong Kong, August 20-23, 2014.

[3] Y. Shen, H. Duan, J. Fang, and H. Li, "Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals," in Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Florence, Italy, May 4-9, 2014.

[2] Y. Shen, J. Fang, H. Li, "One-bit compressive sensing and source localization in wireless sensor networks," in Proc. of IEEE China Summit and Intl. Conf. on Signal and Information Processing, Beijing, China, July 6-10, 2013.

[1] Y. Shen, J. Fang, H. Li, Z. Chen, "A one-bit reweighted iterative algorithm for sparse signal recovery," in Proc. of Intl. Conf. on Acoust., Speech, and Signal Processing, Vancouver, BC, Canada, May 26-31, 2013. (SPS Travel Grant Award)