Representative Publications

 

  1. Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization, Journal of Machine Learning Research, conditionally accepted with minor revisions, 2022,
           J. Ma and S. Fattahi
  2. Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition, ICLR 2023,
          Jianhao Ma, Lingjun Guo, and Salar Fattahi
  3. Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution, NeurIPS 2022, Spotlight (top 3%),
          Jianhao Ma and Salar Fattahi
  4. A Graph-based Decomposition Method for Convex Quadratic Optimization with Indicators, Mathematical Programming, 2022,
           P. Liu, S. Fattahi, A. Gomez, and S. Küçükyavuz
           – INFORMS Computing Society Best Student Paper Award, 2022.
  5. Preconditioned Gradient Descent for Over-parameterized Nonconvex Matrix Factorization, NeurIPS 2021,
           G. Zhang, S. Fattahi, R.Y. Zhang
  6. Scalable Inference of Sparsely-changing Gaussian Markov Random FieldsNeurIPS 2021,
           S. Fattahi and A. Gomez
  7. Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis, Journal of Machine Learning Research, 2020,
           S. Fattahi and S. Sojoudi
  8. Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions, Journal of Machine Learning Research, 2019,
           S. Fattahi and S. Sojoudi
           – INFORMS Data Mining Best Paper Award, 2018.
           – Katta G. Murty Best Paper Award, 2018.
  9. Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion, ICML 2018,
           R. Y. Zhang, S. Fattahi and S. Sojoudi