Processing math: 12%

Sohail Bahmani


Research Vignette

Publications

  • In Review/Revision
  • Journal Paper
  • Conference Paper
  • Tech. Report/Unpublished

In Review/Revision

  1. S. Bahmani, “Variational tail bounds for norms of random vectors and matrices,” 2025.
    arXiv
  2. S. Bahmani, “A fundamental accuracy—robustness trade-off in regression and classification,” 2024.
    arXiv
  3. S. Bahmani, “Instance-dependent uniform tail bounds for empirical processes,” 2022.
    arXiv

2024

  1. S. Kim, S. Bahmani, K. Lee, “Max-linear regression by convex programming,” IEEE Trans. Info. Theory, 2024.
    IEEEXplorearXiv

2021

  1. S. Bahmani, K. Lee, “Low-rank matrix estimation from rank-one projections by unlifted convex optimization”, SIAM J. on Matrix Analysis and Applications, 2021.
    arXivSIAM
  2. S. Bahmani, “Nearly optimal robust mean estimation via empirical characteristic function,” Bernoulli, 27(3): 2139–2158, 2021.
    arXivProj. Euclid
  3. B. Ancelin, S. Bahmani, J. Romberg, “Decentralized feature-distributed optimization for generalized linear models,”, 2021.
    arXiv

2020

  1. S. Bahmani, J. Romberg, “Convex programming for estimation in nonlinear recurrent models,” Journal of Machine Learning Research, (235):1–20, 2020.
    arXivJMLRCode
  2. K. Lee, S. Bahmani, J. Romberg, Y. Eldar, “Phase retrieval of low-rank matrices by anchored regression,” Information and Inference: A Journal of the IMA, 2020.
    arXivOxford Journals

2019

  1. S. Bahmani, “Estimation from nonlinear observations via convex programming, with application to bilinear regression,” Electronic J. of Statistics, 13(1): 1978–2011. 2019.
    arXivProj. Euclid

2018

  1. S. Bahmani and J. Romberg, “Solving equations of random convex functions via anchored regression,” Foundations of Computational Mathematics, 19(4):813–841, 2019.
    arXivSpringer
  2. S. Bahmani, J. Romberg, P. Tetali, “Algebraic connectivity under site percolation in finite weighted graphs,” IEEE Trans. on Network Science and Engineering, 5(2):86–91, 2018. arXivIEEEXplore

2017

  1. S. Bahmani and J. Romberg, “A flexible convex relaxation for phase retrieval,” Electronic Journal of Statistics, 11(2):5254–5281, 2017. (This is an extended version of the AISTATS’17 paper.)
    Proj. Euclid
  2. S. Bahmani and J. Romberg, “Phase retrieval meets statistical learning theory: A flexible convex relaxation,” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS'17), vol. 54 of Proceedings of Machine Learning Research , pp. 252–260. (Best paper award) arXivPMLR

2016

  1. S. Bahmani and J. Romberg, “Near-optimal estimation of simultaneously sparse and low-rank matrices from nested linear measurements,” Information and Inference: A Journal of the IMA 5(3):331–351, 2016.
    arXivOxford Journals
  2. S. Bahmani, P. Boufounos, and B. Raj, “Learning model-based sparsity via projected gradient descent,” IEEE Trans. Info. Theory, 62(4):2092–2099, 2016.
    arXivIEEEXplore

2015

  1. S. Bahmani and J. Romberg, “Sketching for simultaneously sparse and low-rank covariance matrices,” in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'15), IEEE 6th International Workshop on, pp. 357–360, Cancun, Mexico, Dec. 2015.arXivIEEEXplore
  2. S. Bahmani and J. Romberg, “Efficient compressive phase retrieval with constrained sensing vectors,” in Advances in Neural Information Processing Systems (NIPS'15), vol. 28, pp. 523–531, Montréal, Canada, Dec. 2015.
    arXivNIPS
  3. S. Bahmani and J. Romberg, “Lifting for blind deconvolution in random mask imaging: Identifiability and convex relaxation,” SIAM Journal on Imaging Sciences, 8(4):2203–2238, 2015.
    arXivSIAM
  4. S. Bahmani and J. Romberg, “Compressive deconvolution in random mask imaging,” IEEE Trans. on Computational Imaging, 1(4):236–246, 2015.
    arXivIEEEXplore

2013

  1. S. Bahmani, B. Raj, and P. T. Boufounos, “Greedy sparsity-constrained optimization,” Journal of Machine Learning Research, 14(3):807–841, 2013.
    arXivJMLRCode
  2. S. Bahmani, P. Boufounos, and B. Raj, “Robust 1-bit compressive sensing via gradient support pursuit,” Apr. 2013.
    arXiv

2012

  1. S. Bahmani, B. Raj, “A unifying analysis of projected gradient descent for \ell_p-constrained least squares,” Applied and Computational Harmonic Analysis, 34(3):366–378, 2012.
    arXivElsevier

2011

  1. S. Bahmani, P. Boufonos, and B. Raj, “Greedy sparsity-constrained optimization,” in Conf. Record of the 45th Asilomar Conference on Signals, Systems, and Computers (ASILOMAR'11), pp. 1148–1152, Pacific Grove, CA, Nov. 2011. IEEEXploreSlidesCode

2010

  1. S. Bahmani, I. Bajić, and A. HajShirmohammadi, “Joint decoding of unequally protected JPEG2000 images and Reed-Solomon codes,” IEEE Trans. Image Processing, 19(10):2693–2704, Oct. 2010.
    IEEEXplore

2009

  1. S. Bahmani, I. Bajić, and A. HajShirmohammadi, “Improved joint source channel decoding of JPEG2000 images and Reed-Solomon codes,” Proc. IEEE ICC'09, Dresden, Germany, Jun. 2009.
    IEEEXplore

2008

  1. S. Bahmani, I. Bajić, A. HajShirmohammadi, “Joint source channel decoding of JPEG2000 images with unequal loss protection,” Proc. IEEE ICASSP'08, pp. 1365–1368, Las Vegas, NV, Mar. 2008.
    IEEEXplore

Thesis

  • S. Bahmani, Algorithms for sparsity-constrained optimization, PhD dissertation, Department of Electrical & Computer Engineernig, Carnegie Mellon University, Pittsburgh, PA, Feb. 2013.
    PDF
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