Xinyue Shen

Xinyue Shen

Postdoc researcher

I am a visiting post-doc at Stanford University, and my advisor is Professor Stephen Boyd. Previously I was a post-doc at the Chinese University of Hong Kong (Shenzhen) advised by Professor Shuguang Cui. I received Ph.D. degree and B.E. degree in Electronic Engineering from Tsinghua University in 2018 and 2013, respectively, and my Ph.D. advisor was Professor Yuantao Gu. My research interests include optimization algorithms and applications in statistics, signal processing, and machine learning.

Contact

Email: xinyueshen(at)outlook(dot)com

Publications

Optimization algorithms

  • X. Shen, A. Ali, S. Boyd. Minimizing Oracle-Structured Composite Functions. Optimization and Engineering, 2022. paper and code
  • Q. Liu, X. Shen, Y. Gu. Linearized ADMM for Nonconvex Nonsmooth Optimization with Convergence Analysis. IEEE Access, 2019. paper
  • X. Shen, S. Diamond, M. Udell, Y. Gu, S. Boyd. Disciplined Multi-Convex Programming. IEEE Chinese Control and Decision Conference, Outstanding Youth Paper Award, 2017. paper and code
  • X. Shen, S. Diamond, Y. Gu, S, Boyd. Disciplined Convex-Concave Programming. IEEE Conference on Decision and Control, 2016. paper and code

Stochastic control

  • X. Shen, S. Boyd. Incremental Proximal Multi-Forecast Model Predictive Control. 2021. paper and code

Statistics

  • G. Walther, A. Ali, X. Shen, S. Boyd. Confidence Bands for a Log-concave Density. accepted by Journal of Computational and Graphical Statistics, 2022. paper code

Sparse regression and compressed sensing

  • C. Yang, X. Shen, H. Ma, B. Chen, H.C. So. Weakly Convex Regularized Robust Sparse Recovery Methods with Theoretical Guarantees. IEEE Transactions on Signal Processing, 2019.
  • X. Shen, Y. Gu. Nonconvex Sparse Logistic Regression with Weakly Convex Regularization, IEEE Transactions on Signal Processing, 2018.
  • X. Shen, Y. Gu. Nonconvex Sparse Logistic Regression via Proximal Gradient Descent. IEEE International Conference on Acoustics, Speech and Signal Processing, 2018.
  • C. Yang, X. Shen, H. Ma, Y. Gu, H. C. So. Sparse Recovery Conditions and Performance Bounds for l_p-Minimization. IEEE Transactions on Signal Processing, 2018.
  • X. Shen, L. Chen, Y. Gu, H. C. So. Square-Root Lasso With Nonconvex Regularization: An ADMM Approach. IEEE Signal Processing Letters, 2016.
  • X. Shen, J. Romberg, Y. Gu. Robust Off-grid Recovery from Compressed Measurements. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.

Convex optimization applications

  • N. Moehle, X. Shen, Z.-Q. Luo, S. Boyd. A Distributed Method for Optimal Capacity Reservation. Journal of Optimization Theory and Applications, 2019. paper and code
  • K. Qiu, X. Mao, X. Shen, X. Wang, T. Li, Y. Gu. Time-varying Graph Signal Reconstruction. IEEE Journal of Selected Topics in Signal Processing, 2017.
  • X. Shen, Y. Gu. Anti-sparse representation for continuous function by dual atomic norm with application in OFDM. IEEE Global Conference on Signal and Information Processing, 2015.

Subspace and manifold

  • X. Shen, Y. Jiao, Y. Gu. Subspace Data Visualization With Dissimilarity Based on Principal Angle. IEEE Data Science Workshop, 2018.
  • Y. Jiao, X. Shen, G. Li, Y. Gu. Subspace Principal Angle Preserving Property of Gaussian Random Projection. IEEE International Conference on Acoustics, Speech and Signal Processing, 2018.
  • L. Meng, X. Shen, Y. Gu. Sparse Subspace Clustering using Square-root Penalty. International Conference on Digital Signal Processing, 2017.
  • X. Shen, H. Krim, Y. Gu. Beyond Union of Subspaces: Subspace Pursuit on Grassmann Manifold for Data Representation. IEEE International Conference on Acoustics, Speech and Signal Processing, 2016.
  • X. Shen, Y. Gu. Restricted Isometry Property of Subspace Projection Matrix Under Random Compression. IEEE Signal Processing Letters, 2015.
  • X. Shen, Y. Gu. Subspace Projection Matrix Completion on Grassmann Manifold. IEEE International Conference on Acoustics, Speech and Signal Processing, 2015.

Software

  • OSMM, a Python package for oracle-structured minimization method
  • DCCP, a Python package for disciplined convex-concave programming
  • DMCP, a Python package for disciplined multi-convex programming

Selected Professional Services

Journal reviewer

  • Journal of Machine Learning Research
  • IEEE Transactions on Signal Processing
  • IEEE Signal Processing Letters
  • IEEE Access
  • Electronics

Conference reviewer

  • International Conference on Learning Representations (ICLR), 2022
  • International Conference on Machine Learning (ICML), 2021, 2022
  • Conference on Neural Information Processing Systems (NeurIPS), 2021
  • IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018 - 2022