Publications

Summary: 30 journal papers, and 46 conference papers .    Google Scholar  

The underlined author is a UCI student I advise. is a student I advice

Preprints

[P4] R. Du, D. Muthirayan, P. P Khargonekar, Y. Shen*, "Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach" 

[P3] Ö. D. Köse and Y. Shen, "FairGAT: Fairness-aware Graph Attention Networks," March 2023

[P2 J. Xie, Y. Liu, and Y. Shen, "Explaining Dynamic Graph Neural Networks via Relevance Back-propagation," July 2022

[P1] P. M. Ghari, Y. Shen "Online Federated Model Selection," November 2021    

Journal papers 

After UCI 

[J30] Ö. D. Köse and Y. Shen*, "FairGAT: Fairness-aware Graph Attention Networks,"  IEEE Transactions on Knowledge and Data Engineering, Feb 2024.

[J29] P. M. Ghari, and Y. Shen*, "Budgeted Online Model Selection and Fine-Tuning via Federated Learning," the Transactions on Machine Learning Research, Jan 2024.

[J28] O. D. Kose, G. Mateos and Y. Shen*, ”Fairness-aware Graph Filter Design,” IEEE Journal of Selected Topics in Signal Processing, Jan 2024.

[J27] M. Parsanasab, C. Hayakawa, J. Spanier, Y. Shen, V. Venugopalan "Analysis of Relative Error in Perturbation Monte Carlo Simulations of Radiative Transport," the Journal of Biomedical Optics.

[J26] O. D. Kose and Y. Shen*, "Fast&Fair: Training Acceleration and Bias Mitigation for GNNs," the Transactions on Machine Learning Research (TMLR), May 2023 .

[J25] O. D. Kose and Y. Shen*, "Demystifying and Mitigating Bias for Node Representation Learning," IEEE Transactions on Neural Networks and Learning Systems (TNNLS) , April 2023. 

[J24] P.M. Ghari and Y. Shen*, "Online Learning with Uncertain Feedback Graphs," IEEE Transactions on Neural Networks and Learning Systems, 2023.

[J23] P.M. Ghari and Y. Shen*, "Graph-Aided Online Multi-Kernel Learning," Journal of Machine Learning Research (JMLR), 2023.

[J22] Ö. D. Köse and Y. Shen*, "Fairness-aware Graph Contrastive Learning," IEEE Transactions on Signal and Information Processing over Networks, May 2022. 

[J21] A. Celikkanat, Y. Shen, F. D. Malliaros, "Multiple Kernel Representation Learning on Networks", IEEE Transactions on Knowledge and Data Engineering, May 2022.

[J20] M. Li, S. Chen, Y. Shen, G. Liu, I. W. Tsang, Y. Zhang  "Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network," IEEE Transactions on Neural Networks and Learning Systems, 2022.

[J19] Y. Shen*, S. K.Bidhendi, and H. Jafarkhani, “Distributed and Quantized Online Multiple Kernel Learning,” IEEE Transactions on Signal Processing, 2021. 

[J18] 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, vol. 6, no. 1, pp. 271-283, December 2020.

[J17] E. Ceci, Y. Shen, G. B. Giannakis, and S. Barbarossa, "Graph-based Learning under Perturbations via Total Least-Squares,"  IEEE Transactions on Signal Processing, vol. 68, no. 1, pp. 2870--2882, December 2020.

[J16] Y. Shen, G. B. Giannakis, and B. Baingana, "Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks," IEEE Transactions on Signal Processing, vol. 67, no. 20, pp. 5325-5339, October 2019. [pdf]

Before UCI

[J15] Y. Shen, G. Leus and G. B. Giannakis, “Online Graph-Adaptive Learning with Scalability and Privacy,” IEEE Transactions on Signal Processing, May 2019. [pdf]

[J14] 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]

[J13] 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]

[J12] 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]

[J11] 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]

[J10] 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] 

[J9] 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]

[J8] 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]

[J7] 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]

[J6] 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]

[J5] 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] 

[J4] 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]  

[J3] 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] 

[J2] 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]

[J1] 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

After UCI

     [C46] B. Tian, R. Du, Y. Shen, ``Fair Vision Transformer via Adaptive Masking,'' the European Conference on Computer Vision (ECCV 2024)

[C45] O. D. Kose, G, Mateos, Y. Shen, “Filtering as Rewiring for Bias Mitigation on Graphs,” IEEE Sensor Array and Multichannel Signal Processing, Oregon, July, 2024.

[C44] O. D. Kose, Y. Shen, G, Mateos, “Fairness-Aware Graph Filter Design,” Proc. of Asilomar Conf. on Sig., Systems, and Computers , Pacific Grove,CA, November 1-4, 2023. (Best Student Paper Finalist)

     [C43] P. M. Ghari, Y. Shen, “Online Multi-Kernel Learning with Graph-Structured Feedback,” Proc. of Intl. Conf. on Machine Learning (ICML), Vienna, Austria, July 12-18, 2020.[link]

     [C42] O. D. Kose, Y. Shen, “Dynamic Fair Node Representation Learning,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June, 2023.

     [C41] O. D. Kose, Y. Shen, “Fairness-Aware Dimensionality Reduction,” European Signal Processing Conference (EUSIPCO), September, 2023.

[C40] D. Muthirayan, R. Du, Y. Shen, P. P. Khargonekar, “Adaptive Control of Unknown Time Varying Dynamical Systems with Regret Guarantees,” IEEE    Conference on Decision and Control 2023.

[C39] M. Sajid, Y. Shen, Y. Shoukry, “Model Extraction Attacks Against Reinforcement Learning Based Controller” , IEEE Conference on Decision and Control, 2023

[C38] P. M. Ghari, Y. Shen*, "Personalized Online Federated Multi-Kernel Learning," NeurIPS 2022

[C37] R. Du, Y. Shen*, “Fairness-aware user classification in Power Grids,” Proc. of European Signal Processing Conference, 2022.

[C36] O. D. Kose, Y. Shen*, “Fairness-aware adaptive network link prediction,” Proc. of European Signal Processing Conference, 2022.

[C35] P. M. Ghari, Y. Shen*, “Graph-Assisted Communication-Efficient Federated Learning,” Proc. of European Signal Processing Conference, 2022.

[C34] O. D. Kose, Y. Shen*, “Fairness-aware Selective Sampling on Attributed Graphs,” Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing, 2022.

[C33] P. M. Ghari, Y. Shen*, “Online Learning with Probabilistic Feedback,” Proc. of Intl. Conf. on Acoustics, Speech, and Sig. Process., 2022.

[C32] Z. Zong, Y. Shen*, ”Online Multi-hop Information based Kernel Learning over Graphs,” Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing, Toronto, Canada, June 6-11, 2021.

[C31] P. M. Ghari, Y. Shen*, "Graph-aided Online Learning with Expert Advice," Proc. of Asilomar Conf. on Sig., Systems, and Computers , Pacific Grove, CA, November 1-4, 2020.

[C30] P. M. Ghari, Y. Shen*, "Online Multi-Kernel Learning with Graph-Structured Feedback," Proc. of Intl. Conf. on Machine Learning (ICML), Vienna, Austria, July 12-18, 2020.

[C29] Y. Shen*, "Differentially Private Nonlinear Canonical Correlation Analysis" Sensor Array and Multichannel Signal Processing Workshop, June 8-11, 2020.

[C28] Q. Lu, G. V. Karanikolas, Y. Shen, and G. B. Giannakis, "Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability," Proc. of 23rd Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, June 3-5, 2020.

[C27] S. Barrash, Y. Shen, and G. B. Giannakis, “Scalable and Adaptive kNN for Regression over Graphs,” Proc. of CAMSAP Conf, Guadeloupe, West Indies, Dec. 15-18, 2019. (Best Student Paper Finalist)

Before UCI

[C26] Y. Shen, and Geert Leus, “Scalable Learning with Privacy over Graphs,” Proc. of Data Science Workshop, Minneapolis, MN, June 2-5, 2019.

[C25] 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.

[C24] 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.

[C23] 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.

[C22] 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.

[C21] 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.

[C20] 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.

[C19] 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.

[C18] 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)

[C17] 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)

[C16] 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. 

[C15] 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.

[C14] 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)

[C13] 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. 

[C12] 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)

[C11] 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. 

[C10] Y. Shen and G. B. Giannakis, "Online Dictionary Learning for Large-Scale Binary Data," Proc. of EUSIPCO, Budapest, Hungary, Sept. 2016. 

[C9] 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)

[C8] 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.

[C7] 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.

[C6] 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.

[C5] 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.

[C4] 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.

[C3] 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.

[C2] 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. 

[C1] 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)