Ying Sun
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Publications

Book Chapter

  1. G. Scutari and Y. Sun,"Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization," C.I.M.E. Lecture Notes in Mathematics, Springer Verlag Series, 2018. Arxiv.

Journal Papers

  1. Y. Ji, G. Scutari*, Y. Sun*, and H. Honnappa, "Distributed Sparse Regression via Penalization," (accepted) Journal of Machine Learning Research. (*equal contribution)

  2. Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, "Distributed (ATC) Gradient Descent for High Dimension Sparse Regression," IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5253-5276, Aug. 2023.

  3. Y. Sun, A. Daneshmand, and G. Scutari, "Distributed optimization based on gradient-tracking revisited: enhancing convergence rate via surrogation," SIAM Journal on Optimization, vol. 32, pp. 354-385, 2022.

  4. J. Xu, Y. Tian, Y. Sun, G. Scutari, "Distributed algorithms for composite optimization: Unified framework and convergence analysis," IEEE Transactions on Signal Processing, vol. 69, pp. 3555-3570, June 2021.

  5. A. Breloy, S. Kumar, Y. Sun, and D. P. Palomar, "Majorization-Minimization on the Stiefel Manifold with Application to Robust Sparse PCA," IEEE Transactions on Signal Processing, vol. 69, pp. 1507-1520, Feb. 2021.

  6. X. Yu, D. Xu, Y. Sun, D. W. K. Ng and R. Schober, "Robust and secure wireless communications via intelligent reflecting surfaces," IEEE Journal on Selected Areas in Communications, vol. 38, no. 11, pp. 2637-2652, Nov. 2020.
    2023 IEEE Communications Society Leonard G. Abraham Prize

  7. I. Notarnicola*, Y. Sun*, G. Scutari, G. Notarstefano, "Distributed big-data optimization via block-wise gradient tracking," IEEE Transactions on Automatic Control, vol. 66, no. 5, pp. 2045-2060, May 2021. (*equal contribution)

  8. Y. Tian, Y. Sun, and G. Scutari, "Achieving linear convergence in distributed asynchronous multi-agent optimization," IEEE Transactions on Automatic Control, vol. 65, no. 12, pp. 5264-5279, Dec. 2020.

  9. A. Daneshmand, Y. Sun, G. Scutari, F. Facchinei, and Brian M. Sadler, "Decentralized dictionary learning over time-varying digraphs," Journal of Machine Learning Research, vol. 20, no. 139, pp. 1-62, Sept. 2019. ArXiv.

  10. G. Scutari and Y. Sun, "Distributed nonconvex constrained optimization over time-varying digraphs," Mathematical Programming, Series B, vol. 176, no. 1, pp. 497-544, July 2019. ArXiv. The order of the authors is alphabetical.

  11. S. Shen, Y. Sun, S. Song, D. P. Palomar, and R. D. Murch, "Successive boolean optimization of planar pixel antennas," IEEE Transactions on Antennas and Propagation, vol. 65, no. 2, pp. 920-925, Feb. 2017.

  12. Y. Sun, P. Babu, and D. P. Palomar, "Majorization-minimization algorithms in signal processing, communications, and machine learning," overview article, IEEE Transactions on Signal Processing, vol. 65, no. 3, pp. 794-816, Feb. 2017.
    2020 SPS Young Author Best Paper Award

  13. K. Benidis, Y. Sun, P. Babu, D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016.

  14. Y. Sun, A. Breloy, P. Babu, D. P. Palomar, F. Pascal, and G. Ginolhac, "Low-complexity algorithms for low rank clutter parameters estimation in radar systems," IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1986-1998, April 2016. pdf

  15. Y. Sun, P. Babu, and D. P. Palomar, "Robust estimation of structured covariance matrix for heavy-tailed elliptical distributions," IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3576-3590, July 2016. ArXiv.

  16. Y. Sun, P. Babu, and D. P. Palomar, "Regularized robust estimation of mean and covariance matrix under heavy-tailed distributions," IEEE Transactions on Signal Processing, vol. 63, no. 12, pp. 3096-3109, June 2015. pdf

  17. Y. Sun, P. Babu, and D. P. Palomar, "Regularized Tyler's scatter estimator: existence, uniqueness, and algorithms," IEEE Transactions on Signal Processing, vol. 62, no. 19, pp. 5143-5156, Oct. 2014. pdf

Conference Papers

  1. Y. Huang, Y. Sun, Z. Zhu, C. Yan, and J. Xu, "Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology," International Conference on Machine Learning, PMLR 162:9310-9345, 2022.

  2. Y. Guo, Y. Sun, R. Hui, Y. Gong, "Hybrid local SGD for federated learning with heterogeneous communications," International Conference on Learning Representations (Spotlight), 2021.

  3. J. Xu, Y. Tian, Y. Sun, G. Scutari, "Accelerated primal-dual algorithms for distributed smooth convex optimization over networks," in Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTAT), Online, Aug. 26-28, 2020, pp. 2381-2391.

  4. J. Xu, Y. Sun, Y. Tian and G. Scutari, "A unified contraction analysis of a class of distributed algorithms for composite optimization," in Proc. of the 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Le gosier, Guadeloupe, Dec. 15-18, 2019, pp. 485-489.

  5. Y. Tian, Y. Sun, B. Du, and G. Scutari, "ASY-SONATA: Achieving geometric convergence for distributed asynchronous optimization," Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Oct. 2-5, 2018.

  6. I. Notarnicola*, Y. Sun*, G. Scutari, G. Notarstefano, "Distributed big-data optimization via block-iterative convexification and averaging," in Proc. of the 56th IEEE Conference on Decision and Control (CDC17), Melbourne, Australia, Dec. 12-15, 2017, pp. 2281-2288. (*equal contribution)

  7. I. Notarnicola*, Y. Sun*, G. Scutari, and G. Notarstefano, "Distributed Big-Data Optimization via Block Communications," in Proc. of the 2017 IEEE workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP17), Curacao, Dutch Antilles, Dec. 10-13, 2017, pp. 1-5. (*equal contribution)
    Best Student Paper Award

  8. Y. Sun and G. Scutari, "Distributed nonconvex optimization for sparse representation," in Proc. of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP17), New Orleans, Mar. 2017, pp. 4044-4048. ArXiv.

  9. A. Daneshmand, Y. Sun, G. Scutari, and F. Facchinei, "D2L: Decentralized dictionary learning over dynamic networks," in Proc. of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP17), New Orleans, Mar. 2017, pp. 4084-4088.

  10. Y. Sun, G. Scutari, and D. P. Palomar, "Distributed nonconvex multiagent optimization over time-varying networks," in Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Asilomar, Nov. 2016, pp. 788-794. ArXiv.

  11. A. Breloy, Y. Sun, P. Babu, and D. P. Palomar, "Block majorization-minimization algorithms for low-rank clutter subspace estimation," in Proc. 24th European Signal Processing Conference (EUSIPCO16), Budapest, Aug. 2016, pp. 2186-2190.

  12. A. Breloy, Y. Sun, P. Babu, G. Ginolhac, D. P. Palomar, and F. Pascal, "A robust signal subspace estimator," in Proc. IEEE Statistical Signal Processing Workshop (SSP16)," Palma de Mallorca, June 2016, pp. 1-4.

  13. K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse Eigenvectos: A Procrustes Problem," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP16), Shanghai, China, March 20-25, 2016.

  14. Y. Sun, P. Babu, and D. P. Palomar, "Robust estimation of structured covariance matrix for heavy-tailed distributions," in Proc. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP15), Brisbane, April 2015, pp. 5693-5697.

  15. Y. Sun, P. Babu, and D. P. Palomar, "Regularized Robust Estimation of Mean and Covariance Matrix under Heavy Tails and Outliers," in Proc. IEEE 8th Sensor Array and Multichannel Signal Process. Workshop (SAM14), A Coruna, June 2014, pp. 125-128.