Rayan Saab

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*Papers on the arXiv Most (but not all) journal paper preprints can be found on the arXiv, here.

Preprints/Manuscripts in Preparation

1. GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation with S. Zhang, preprint
   [arXiv]
2. The Measure of Deception: An Analysis of Data Forging in Machine Unlearning with R. Dixit, Y. Hui, preprint
   [arXiv]
3. Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos, with H. Zhang, S.Zhang, I. Colbert, preprint
   [arXiv]

Journal Papers

1. SPFQ: A Stochastic Algorithm and Its Error Analysis for Neural Network Quantization, with J. Zhang, Information and Inference: A Journal of the IMA, 2026
   [arXiv]
2. Beacon: Post-Training Quantization with Integrated Grid Selection with S.Zhang, IEEE Signal Processing Letters, accepted Jan. 2026
   [arXiv]

3. Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks, with S. Zhang, Applied and Computational Harmonic Analysis, 82, Feb. 2026
   [arXiv]

4. Accumulator-Aware Post-Training Quantization, with I. Colbert, F. Grob, G. Franco, J. Zhang, Transactions on Machine Learning Research, December 2025
   [Journal version] [arXiv] [code]

5. Unified Stochastic Framework for Neural Network Quantization and Pruning, with H. Zhang, Applied and Computational Harmonic Analysis, 77, Oct. 2025
   [arXiv]

6. Random Vector Functional Link Networks for Function Approximation on Manifolds, with D. Needell, A.A. Nelson, P. Salanevich, Frontiers in Applied Mathematics and Statistics (10), 2024
   [arXiv]

7. Post-training Quantization for Neural Networks with Provable Guarantees, with J. Zhang, Y. Zhou, SIAM Journal on Mathematics of Data Science 5 (2), 373-399, 2023
   [arXiv], [Code], [Talk]

8. Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features, with J. Zhang, H. Kannan, A. Cloninger, Information and Inference: A Journal of the IMA
   [arXiv]

9. A simple approach for quantizing neural networks, with J. Maly, Applied and Computational Harmonic Analysis 66, 138-150, 2023
   [arXiv]

10. On the Number of Faces and Radii of Cells Induced by Gaussian Spherical Tessellations, with E. Lybrand, A. Ma, Applied and Computational Harmonic Analysis, 56, 176-188, 2022.
    [arXiv]

11. A Greedy Algorithm for Quantizing Neural Networks, with E. Lybrand, Journal of Machine Learning Research 22(156), 1-38, 2021.
    [arXiv], [Code] [Talk]

12. On the $\ell^\infty$-norms of the Singular Vectors of Arbitrary Powers of a Difference Matrix with Applications to Sigma-Delta Quantization, with T. Faust, M. Iwen, R. Wang, Linear Algebra and its Applications, 2021
    [arXiv]

13. Admissible Measurements and Robust Algorithms for Ptychography, with B. Preskitt, Journal of Fourier Analysis and Applications 27 (2), 1-39, 2021.
    [arXiv]

14. A direct solver for the phase retrieval problem in ptychographic imaging , with N. Sissouno, F. Boßmann, F. Filbir, M. Iwen, M. Kahnt, C. Schroer, W. zu Castell, Mathematics and Computers in Simulation 176, 292-300, 2020.
    [arXiv]

15. Fast binary embeddings, and quantized compressed sensing with structured matrices, with T. Huynh. Communications on Pure and Applied Mathematics 73 (1), 110-149, 2020.
    [arXiv], [Talk]

16. Quantized Compressed Sensing for Partial Random Circulant Matrices with J. Feng, F. Krahmer. Applied and Computational Harmonic Analysis 47 (3), 1014-1032, 2019.
    [arXiv]

17. Simple Classification using Binary Data with D. Needell, T. Woolf. The Journal of Machine Learning Research 19 (1), 2487-2516, 2018.
    [arXiv]

18. Phase Retrieval from Local Measurements: Improved Robustness via Eigenvector-Based Angular Synchronization with M. A. Iwen, B. Preskitt, A. Viswanathan. Applied and Computational Harmonic Analysis 48(1), 415-444, 2020.
    [arXiv]

19. Quantization for Low-Rank Matrix Recovery, with E. Lybrand. Information and Inference: A Journal of the IMA 8 (1), 161-180, 2019.
    [arXiv]

20. From compressed sensing to compressed bit-streams: practical encoders, tractable decoders with R. Wang, O. Yilmaz. IEEE Transactions on Information Theory 64 (9), 6098-6114, 2017.
    [arXiv]

21. Weighted \ell_1-Minimization for Sparse Recovery under Arbitrary Prior Information with D. Needell and T. Woolf. Information and Inference: A Journal of the IMA 6 (3), 284-309, 2017.
    [arXiv ] [Code]

22. Quantization of compressive samples with stable and robust recovery with R. Wang, O. Yilmaz. Applied and Computational Harmonic Analysis, 44(1), Pages 123-143, 2018.
    [arXiv ]

23. One-bit compressive sensing with norm estimation with K. Knudson, R. Ward. Accepted for publication in IEEE Information Theory, 2016.
    [arXiv] [Journal]

24. Recovery Analysis for Weighted \ell_1-Minimization Using a Null Space Property with H. Mansour. Applied and Computational Harmonic Analysis 43 (1), 23-38, 2017.
    [arXiv] [Journal]

25. A Deterministic Analysis of Decimation for Sigma-Delta Quantization of Bandlimited Functions, with I. Daubechies. IEEE Signal Processing Letters, Volume 22, No. 11, November 2015.
    [ arXiv ] [Journal]

26. Finite sample posterior concentration in high-dimensional regression, with N. Strawn, A. Armagan, L. Carin, D. Dunson. Information and Inference, 31 pages, appeared online May 2014
    [arXiv]

27. Sigma-Delta quantization of sub-Gaussian frame expansions and its application to compressed sensing, with Felix Krahmer and O. Yilmaz. Information and Inference, 19 pages, appeared online February 2014
    [arXiv][ Journal]

28. Near-optimal encoding of finite frame expansions, with M. Iwen. Journal of Fourier Analysis and Applications, Volume 19, Issue 6, pp 1255-1273, December 2013
    [arXiv] [Journal]

29. Sobolev duals for random frames and Sigma-Delta quantization of compressed sensing measurements, with S. Gunturk, M. Lammers, A. Powell, and O. Yilmaz., Foundations of Computational Mathematics, Volume 13, Issue 1, pp 1--36, 2013
    [arXiv ] [ Journal]

30. Root-exponential accuracy for coarse quantization of finite frame expansions, with F. Krahmer, and R. Ward. IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1069 -- 1079, 2012
    [Preprint]

31. Recovering Compressively Sampled Signals Using Partial Support Information, with M. P. Friedlander, H. Mansour, and O. Yilmaz. IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1122 -- 1134, 2012
    [arXiv]

32. Sparse recovery by non-convex optimization -- instance optimality , with O. Yilmaz. Applied and Computational Harmonic Analysis, vol. 29, no. 1, pp. 30-–48, July 2010.
    [arXiv] [Journal]

33. Sparco: a testing framework for sparse reconstruction, with E. van den Berg, M.P. Friedlander, G. Hennenfent, F. J. Herrmann, O. Yilmaz. ACM Transactions on Mathematical Software, vol. 35, no. 4, 2009.
    [Preprint (pdf)] [Code]

34. Bayesian wavefield separation by transform-domain sparsity promotion, with D. Wang, O. Yilmaz, F. J. Herrmann. Geophysics, vol. 73, no. 5, 2008.
    [Preprint (pdf)]

35. Underdetermined Anechoic Blind Source Separation via $\ell^q$-Basis-Pursuit, with $q \leq 1$, with O. Yilmaz, M.J. McKeown, R. Abughabieh. IEEE Transactions on Signal Processing, vol. 55, no. 8, pp. 4004-4017, 2007.
    [Preprint (pdf)]

36. Permeation in Gramicidin ion channels by directly estimating the potential of mean force using Brownian dynamics simulations, with V. Krishnamurthy, M. Hoyles, and S.H. Chung. Journal of Computational and Theoretical Nanoscience, 2006, Vol. 3, pp. 702-711.

37. Cortical Muscle Coupling in Parkinson's Disease (PD) Bradykinesia, with M. McKeown, S. Palmer, W. Au, R. McCaig, R. Abu-Gharbieh. Journal of Neural Transmission, 2006, Suppl 70, pp. 31-40.