Jirong Yi - FrontiersBMImaging

Jirong Yi

Frontiers in Compressed Sensing and Medical Imaging

Compressed Sensing Fundamentals and Algorithms

Fan J, Yang Z, Yu M. Understanding implicit regularization in over-parameterized single index model. Journal of the American Statistical Association. 2023 Oct 2;118(544):2315-28. link
Fan J, Yang Z, Yu M. Understanding implicit regularization in over-parameterized single index model. Journal of the American Statistical Association. 2023 Oct 2;118(544):2315-28. link
Machidon AL, Pejović V. Deep learning for compressive sensing: a ubiquitous systems perspective. Artificial Intelligence Review. 2023 Apr;56(4):3619-58. link
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Gao S, Zhang Z, Ma J, Li Z, Zhang S. Correlation-aware mutual learning for semi-supervised medical image segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 98-108). Cham: Springer Nature Switzerland. link
McRae AD, Karnik S, Davenport M, Muthukumar VK. Harmless interpolation in regression and classification with structured features. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 5853-5875). PMLR. link
Mukkamala MC, Fadili J, Ochs P. Global convergence of model function based Bregman proximal minimization algorithms. Journal of Global Optimization. 2022 Aug 1:1-29. link
Liu D, Cevher V, Tran-Dinh Q. A Newton Frank–Wolfe method for constrained self-concordant minimization. Journal of Global Optimization. 2022 Jun 1:1-27. link
Lee Y, Lee S, Won JH. Statistical inference with implicit sgd: proximal robbins-monro vs. polyak-ruppert. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 12423-12454). PMLR. link
Kacham P, Woodruff D. Sketching algorithms and lower bounds for ridge regression. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 10539-10556). PMLR. link
Jin Q, Koppel A, Rajawat K, Mokhtari A. Sharpened quasi-Newton methods: Faster superlinear rate and larger local convergence neighborhood. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 10228-10250). PMLR. link
Heckel R. Provable continual learning via sketched Jacobian approximations. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 10448-10470). PMLR. link
Croce F, Hein M. Adversarial Robustness against Multiple and Single $ l_p $-Threat Models via Quick Fine-Tuning of Robust Classifiers. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 4436-4454). PMLR. link
Mukkamala MC, Fadili J, Ochs P. Global convergence of model function based Bregman proximal minimization algorithms. Journal of Global Optimization. 2022 Aug 1:1-29. link
Liu D, Cevher V, Tran-Dinh Q. A Newton Frank–Wolfe method for constrained self-concordant minimization. Journal of Global Optimization. 2022 Jun 1:1-27. link
Pathak R, Ma C, Wainwright M. A new similarity measure for covariate shift with applications to nonparametric regression. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 17517-17530). PMLR. link
Hurault S, Leclaire A, Papadakis N. Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 9483-9505). PMLR. link
Axiotis K, Sviridenko M. Iterative hard thresholding with adaptive regularization: Sparser solutions without sacrificing runtime. InInternational Conference on Machine Learning 2022 Jun 28 (pp. 1175-1197). PMLR. link
Nouiehed M, Razaviyayn M. Learning deep models: Critical points and local openness. INFORMS Journal on Optimization. 2022 Apr;4(2):148-73. link
Cai TT, Zhang AR, Zhou Y. Sparse group lasso: Optimal sample complexity, convergence rate, and statistical inference. IEEE transactions on information theory. 2022 May 16;68(9):5975-6002. link
Duchi JC, Namkoong H. Learning models with uniform performance via distributionally robust optimization. The Annals of Statistics. 2021 Jun;49(3):1378-406. link
Chen Y, Ma C, Poor HV, Chen Y. Learning mixtures of low-rank models. IEEE Transactions on Information Theory. 2021 Mar 12;67(7):4613-36. link
Zhu Z, Li Q, Tang G, Wakin MB. The global optimization geometry of low-rank matrix optimization. IEEE Transactions on Information Theory. 2021 Jan 5;67(2):1308-31. link
Amir T, Basri R, Nadler B. The trimmed lasso: Sparse recovery guarantees and practical optimization by the generalized soft-min penalty. SIAM journal on mathematics of data science. 2021;3(3):900-29. link
Charoenphakdee N, Cui Z, Zhang Y, Sugiyama M. Classification with rejection based on cost-sensitive classification. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 1507-1517). PMLR. link
Eftekhari A, Tanner J, Thompson A, Toader B, Tyagi H. Sparse non-negative super-resolution—simplified and stabilised. Applied and Computational Harmonic Analysis. 2021 Jan 1;50:216-80. link
Tan SS, Varvitsiotis A, Tan VY. Analysis of optimization algorithms via sum-of-squares. Journal of Optimization Theory and Applications. 2021 Jul;190(1):56-81. link
Pandit MK, Naaz R, Chishti MA. Learning sparse neural networks using non-convex regularization. IEEE Transactions on Emerging Topics in Computational Intelligence. 2021 Mar 8;6(2):287-99. link
Nguyen LM, Tran-Dinh Q, Phan DT, Nguyen PH, Van Dijk M. A unified convergence analysis for shuffling-type gradient methods. Journal of Machine Learning Research. 2021;22(207):1-44. link
Wang JK, Lin CH, Abernethy JD. A modular analysis of provable acceleration via polyak’s momentum: Training a wide relu network and a deep linear network. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 10816-10827). PMLR. link
Wang B, Zhang H, Zhang J, Meng Q, Chen W, Liu TY. Optimizing information-theoretical generalization bound via anisotropic noise of SGLD. Advances in Neural Information Processing Systems. 2021 Dec 6;34:26080-90. link
Song Z, Yu Z. Oblivious sketching-based central path method for linear programming. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 9835-9847). PMLR. link
Nguyen Q. On the proof of global convergence of gradient descent for deep relu networks with linear widths. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 8056-8062). PMLR. link
Liu T, Li Y, Wei S, Zhou E, Zhao T. Noisy gradient descent converges to flat minima for nonconvex matrix factorization. InInternational Conference on Artificial Intelligence and Statistics 2021 Mar 18 (pp. 1891-1899). PMLR. link
Jiang Y, Li Y, Sun Y, Wang J, Woodruff D. Single pass entrywise-transformed low rank approximation. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 4982-4991). PMLR. link
Jiang Y, Li Y, Sun Y, Wang J, Woodruff D. Single pass entrywise-transformed low rank approximation. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 4982-4991). PMLR. link
Asi H, Feldman V, Koren T, Talwar K. Private stochastic convex optimization: Optimal rates in l1 geometry. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 393-403). PMLR. link
Boffi N, Tu S, Slotine JJ. The role of optimization geometry in single neuron learning. InInternational Conference on Artificial Intelligence and Statistics 2022 May 3 (pp. 11528-11549). PMLR. link
Genzel M, Kutyniok G, März M. ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed?. Applied and Computational Harmonic Analysis. 2021 May 1;52:82-140. link
Genzel M, Kutyniok G, März M. ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed?. Applied and Computational Harmonic Analysis. 2021 May 1;52:82-140. link
Zhang X, Liu Y, Cui W. Spectrally sparse signal recovery via Hankel matrix completion with prior information. IEEE Transactions on Signal Processing. 2021 Mar 29;69:2174-87. link
Ahuja K, Shanmugam K, Dhurandhar A. Linear regression games: Convergence guarantees to approximate out-of-distribution solutions. InInternational Conference on Artificial Intelligence and Statistics 2021 Mar 18 (pp. 1270-1278). PMLR. link
Zhao YB, Luo ZQ. Analysis of optimal thresholding algorithms for compressed sensing. Signal Processing. 2021 Oct 1;187:108148. link
Genzel M, Kutyniok G, März M. ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed?. Applied and Computational Harmonic Analysis. 2021 May 1;52:82-140. link
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Pedregosa F, Negiar G, Askari A, Jaggi M. Linearly convergent Frank-Wolfe with backtracking line-search. InInternational conference on artificial intelligence and statistics 2020 Jun 3 (pp. 1-10). PMLR. link
Garber D. On the convergence of stochastic gradient descent with low-rank projections for convex low-rank matrix problems. InConference on Learning Theory 2020 Jul 15 (pp. 1666-1681). PMLR. link
Carmon Y, Duchi JC. First-order methods for nonconvex quadratic minimization. SIAM Review. 2020;62(2):395-436. link
Burke JV, Curtis FE, Wang H, Wang J. Inexact sequential quadratic optimization with penalty parameter updates within the QP solver. SIAM Journal on Optimization. 2020;30(3):1822-49. link
Bonettini S, Prato M, Rebegoldi S. Convergence of Inexact Forward--Backward Algorithms Using the Forward--Backward Envelope. SIAM Journal on Optimization. 2020;30(4):3069-97. link
Banert S, Ringh A, Adler J, Karlsson J, Oktem O. Data-driven nonsmooth optimization. SIAM Journal on Optimization. 2020;30(1):102-31. link
Aybat NS, Fallah A, Gurbuzbalaban M, Ozdaglar A. Robust accelerated gradient methods for smooth strongly convex functions. SIAM Journal on Optimization. 2020;30(1):717-51. link
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Mokhtari A, Koppel A. High-dimensional nonconvex stochastic optimization by doubly stochastic successive convex approximation. IEEE Transactions on Signal Processing. 2020 Oct 26;68:6287-302. link
Luke DR, Martins AL. Convergence Analysis of the Relaxed Douglas--Rachford Algorithm. SIAM Journal on Optimization. 2020;30(1):542-84. link
Liu H, Foygel Barber R. Between hard and soft thresholding: optimal iterative thresholding algorithms. Information and Inference: A Journal of the IMA. 2020 Dec;9(4):899-933. link
Heaton H, Fung SW, Lin AT, Osher S, Yin W. Projecting to manifolds via unsupervised learning. arXiv preprint arXiv:2008.02200. 2020. link
Ghosh A, Kannan R. Alternating minimization converges super-linearly for mixed linear regression. InInternational conference on artificial intelligence and statistics 2020 Jun 3 (pp. 1093-1103). PMLR. link
Fu H, Chi Y, Liang Y. Guaranteed recovery of one-hidden-layer neural networks via cross entropy. IEEE transactions on signal processing. 2020 May 7;68:3225-35. link
Li X, Sun D, Toh KC. An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for linear programming. SIAM Journal on Optimization. 2020;30(3):2410-40. link
Feng X, Yan S, Wu C. The ℓ2, q regularized group sparse optimization: lower bound theory, recovery bound and algorithms. Applied and Computational Harmonic Analysis. 2020 Sep 1;49(2):381-414. link
Farnia F, Zhang JM, David NT. A Fourier-based approach to generalization and optimization in deep learning. IEEE Journal on Selected Areas in Information Theory. 2020 Mar 26;1(1):145-56. link
Dwivedi R, Ho N, Khamaru K, Wainwright MJ, Jordan MI, Yu B. Singularity, misspecification and the convergence rate of EM. The Annals of Statistics. 2020 Dec 1;48(6):3161-82. link
Duchi JC, Hinder O, Naber A, Ye Y. Conic descent and its application to memory-efficient optimization over positive semidefinite matrices. Advances in Neural Information Processing Systems. 2020;33:8308-17. link
del Álamo M, Munk A. Total variation multiscale estimators for linear inverse problems. Information and Inference: A Journal of the IMA. 2020 Dec;9(4):961-86. link
Sarao Mannelli S, Vanden-Eijnden E, Zdeborová L. Optimization and generalization of shallow neural networks with quadratic activation functions. Advances in Neural Information Processing Systems. 2020;33:13445-55. link
Wan A. Uniform RIP Conditions for Recovery of Sparse Signals by $\ell _p\,(0< p\leq 1) $ Minimization. IEEE Transactions on Signal Processing. 2020 Sep 8;68:5379-94. link
Chen Y, Chi Y, Fan J, Ma C, Yan Y. Noisy matrix completion: Understanding statistical guarantees for convex relaxation via nonconvex optimization. SIAM journal on optimization. 2020;30(4):3098-121. link
Tirer T, Giryes R. Generalizing CoSaMP to signals from a union of low dimensional linear subspaces. Applied and Computational Harmonic Analysis. 2020 Jul 1;49(1):99-122. link
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Meng N, Zhao YB. Newton-step-based hard thresholding algorithms for sparse signal recovery. IEEE Transactions on Signal Processing. 2020 Nov 13;68:6594-606. link
Zhang J, Zhao C, Gao W. Optimization-inspired compact deep compressive sensing. IEEE Journal of Selected Topics in Signal Processing. 2020 Mar 2;14(4):765-74. link
Pooladian AA, Finlay C, Hoheisel T, Oberman A. A principled approach for generating adversarial images under non-smooth dissimilarity metrics. InInternational Conference on Artificial Intelligence and Statistics 2020 Jun 3 (pp. 1442-1452). PMLR. link
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Mutny M, Derezinski M, Krause A. Convergence analysis of block coordinate algorithms with determinantal sampling. InInternational Conference on Artificial Intelligence and Statistics 2020 Jun 3 (pp. 3110-3120). PMLR. link
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Yang C, Gu Y, Chen B, Ma H, So HC. Learning proximal operator methods for nonconvex sparse recovery with theoretical guarantee. IEEE Transactions on Signal Processing. 2020 Mar 5;68:5244-59. link
Xu J, Tian Y, Sun Y, Scutari G. Accelerated primal-dual algorithms for distributed smooth convex optimization over networks. InInternational Conference on Artificial Intelligence and Statistics 2020 Jun 3 (pp. 2381-2391). PMLR. link
Van Luong H, Joukovsky B, Deligiannis N. Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1 $-$\ell_1 $ Minimization: Architecture Design and Generalization Analysis. arXiv preprint arXiv:2003.08334. 2020 Mar 18. link
Themelis A, Patrinos P. Douglas--Rachford splitting and ADMM for nonconvex optimization: Tight convergence results. SIAM Journal on Optimization. 2020;30(1):149-81. link
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Liu Q, Wang J. $ L_ {1} $-minimization algorithms for sparse signal reconstruction based on a projection neural network. IEEE Transactions on Neural Networks and Learning Systems. 2015 Oct 26;27(3):698-707. link
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De Sa C, Re C, Olukotun K. Global convergence of stochastic gradient descent for some non-convex matrix problems. InInternational conference on machine learning 2015 Jun 1 (pp. 2332-2341). PMLR. link
Chen W, Li Y. Stable recovery of low-rank matrix via nonconvex Schatten p-minimization. Science China Mathematics. 2015 Dec;58(12):2643-54. link
Jain P, Netrapalli P. Fast exact matrix completion with finite samples. InConference on Learning Theory 2015 Jun 26 (pp. 1007-1034). PMLR. link
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Liu Y, Flynn RT, Kim Y, Wu X. Asymmetric dose–volume optimization with smoothness control for rotating‐shield brachytherapy. Medical physics. 2014 Nov;41(11):111709. link
Iwen MA. Compressed sensing with sparse binary matrices: Instance optimal error guarantees in near-optimal time. Journal of Complexity. 2014 Feb 1;30(1):1-5. link
Yang AY, Zhou Z, Balasubramanian AG, Sastry SS, Ma Y. Fast $\ell_ {1} $-Minimization Algorithms for Robust Face Recognition. IEEE Transactions on Image Processing. 2013 May 13;22(8):3234-46. link
Candes E, Recht B. Simple bounds for recovering low-complexity models. Mathematical Programming. 2013 Oct;141(1):577-89. link
Tang G, Bhaskar BN, Shah P, Recht B. Compressed sensing off the grid. IEEE transactions on information theory. 2013 Aug 7;59(11):7465-90. link
Donoho DL, Johnstone I, Montanari A. Accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising. IEEE transactions on information theory. 2013 Jan 10;59(6):3396-433. link
Jain P, Netrapalli P, Sanghavi S. Low-rank matrix completion using alternating minimization. InProceedings of the forty-fifth annual ACM symposium on Theory of computing 2013 Jun 1 (pp. 665-674). link
Kyrillidis A, Baldassarre L, Halabi ME, Tran-Dinh Q, Cevher V. Structured sparsity: Discrete and convex approaches. InCompressed Sensing and its Applications: MATHEON Workshop 2013 2015 (pp. 341-387). Springer International Publishing. link
Xu Y, Yin W, Wen Z, Zhang Y. An alternating direction algorithm for matrix completion with nonnegative factors. Frontiers of Mathematics in China. 2012 Apr;7:365-84. link
Chandrasekaran V, Recht B, Parrilo PA, Willsky AS. The convex geometry of linear inverse problems. Foundations of Computational mathematics. 2012 Dec;12(6):805-49. link
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Juditsky A, Nemirovski A. On verifiable sufficient conditions for sparse signal recovery via ℓ 1 minimization. Mathematical programming. 2011 Mar;127:57-88. link
Foygel R, Srebro N. Concentration-based guarantees for low-rank matrix reconstruction. InProceedings of the 24th Annual Conference on Learning Theory 2011 Dec 21 (pp. 315-340). JMLR Workshop and Conference Proceedings. link
Chi Y, Scharf LL, Pezeshki A, Calderbank AR. Sensitivity to basis mismatch in compressed sensing. IEEE Transactions on Signal Processing. 2011 Feb 10;59(5):2182-95. link
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Yun S, Toh KC. A coordinate gradient descent method for ℓ 1-regularized convex minimization. Computational Optimization and Applications. 2011 Mar;48:273-307. link
Yang J, Zhang Y. Alternating direction algorithms for \ell_1-problems in compressive sensing. SIAM journal on scientific computing. 2011;33(1):250-78. link
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Deep Learning-Driven Compressed Sensing Fundamentals and Algorithms

Hollmann N, Müller S, Purucker L, Krishnakumar A, Körfer M, Hoo SB, Schirrmeister RT, Hutter F. Accurate predictions on small data with a tabular foundation model. Nature. 2025 Jan 9;637(8045):319-26. link
Yi J, Gao J, Wang T, Wu X, Xu W. Outlier Detection Using Generative Models with Theoretical Performance Guarantees. IEEE transactions on information theory. 2024 Dec 11. link
Chand JR, Jacob M. Multi-scale energy (muse) framework for inverse problems in imaging. IEEE transactions on computational imaging. 2024 Aug 23. link
Shen M, Gan H, Ma C, Ning C, Li H, Liu F. MTC-CSNet: Marrying transformer and convolution for image compressed sensing. IEEE Transactions on Cybernetics. 2024 Feb 26. link
Gan H, Shen M, Hua Y, Ma C, Zhang T. From patch to pixel: A transformer-based hierarchical framework for compressive image sensing. IEEE Transactions on Computational Imaging. 2023 Feb 22;9:133-46. link
Zhong Y, Zhang C, Li J. Image compressed sensing reconstruction via deep image prior with structure-texture decomposition. IEEE Signal Processing Letters. 2023 Feb 2;30:85-9. link
Fei B, Lyu Z, Pan L, Zhang J, Yang W, Luo T, Zhang B, Dai B. Generative diffusion prior for unified image restoration and enhancement. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 9935-9946). link
Zhang Z, Chen M, Wang M, Liao W, Zhao T. Effective minkowski dimension of deep nonparametric regression: function approximation and statistical theories. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 40911-40931). PMLR. link
Zhang H, Li Y, Lu W, Lin Q. On the optimality of misspecified kernel ridge regression. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 41331-41353). PMLR. link
Mao Y, Jiang L, Chen X, Li C. Disc-diff: Disentangled conditional diffusion model for multi-contrast mri super-resolution. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 387-397). Cham: Springer Nature Switzerland. link
Luo H, Bao J, Wu Y, He X, Li T. Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 23033-23044). PMLR. link
Lin B, Ye Y, Zhu B, Cui J, Ning M, Jin P, Yuan L. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122. 2023 Nov 16. link
Kotelnikov A, Baranchuk D, Rubachev I, Babenko A. Tabddpm: Modelling tabular data with diffusion models. InInternational Conference on Machine Learning 2023 Jul 3 (pp. 17564-17579). PMLR. link
Laousy O, Araujo A, Chassagnon G, Paragios N, Revel MP, Vakalopoulou M. Certification of deep learning models for medical image segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 611-621). Cham: Springer Nature Switzerland. link
Lin Z, Liu C, Zhang R, Gao P, Qiu L, Xiao H, Qiu H, Lin C, Shao W, Chen K, Han J. Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models. arXiv preprint arXiv:2311.07575. 2023 Nov 13. link
Choi J, Park Y, Kang M. Restoration based generative models. arXiv preprint arXiv:2303.05456. 2023 Feb 20. link
Daras G, Dagan Y, Dimakis AG, Daskalakis C. Score-guided intermediate layer optimization: Fast Langevin mixing for inverse problems. arXiv preprint arXiv:2206.09104. 2022 Jun 18. link
Li B, Liu X, Hu P, Wu Z, Lv J, Peng X. All-in-one image restoration for unknown corruption. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 17452-17462). link
Lu R, Ye K. Tree-structured dilated convolutional networks for image compressed sensing. IEEE Access. 2022 Sep 14;10:98374-83. link
Chen B, Zhang J. Content-aware scalable deep compressed sensing. IEEE Transactions on Image Processing. 2022 Aug 10;31:5412-26. link
Jia M, Yu L, Bai W, Zhang P, Zhang L, Wang W, Gao F. Single pixel imaging via unsupervised deep compressive sensing with collaborative sparsity in discretized feature space. Journal of Biophotonics. 2022 Jul;15(7):e202200045. link
Zhang K, Hua Z, Li Y, Chen Y, Zhou Y. Ams-net: Adaptive multi-scale network for image compressive sensing. IEEE Transactions on Multimedia. 2022 Aug 15;25:5676-89. link
Shen M, Gan H, Ning C, Hua Y, Zhang T. TransCS: A transformer-based hybrid architecture for image compressed sensing. IEEE Transactions on Image Processing. 2022 Nov 1;31:6991-7005. link
Lorenzana MB, Engstrom C, Chandra SS. Transformer compressed sensing via global image tokens. In2022 IEEE International Conference on Image Processing (ICIP) 2022 Oct 16 (pp. 3011-3015). IEEE. link
Lee B, Ko K, Hong J, Ku B, Ko H. Information bottleneck measurement for compressed sensing image reconstruction. IEEE Signal Processing Letters. 2022 Sep 8;29:1943-7. link
Wang X, Li Y, Zhang H, Shan Y. Towards real-world blind face restoration with generative facial prior. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2021 (pp. 9168-9178). link
Huang W, Hand P, Heckel R, Voroninski V. A provably convergent scheme for compressive sensing under random generative priors. Journal of Fourier Analysis and Applications. 2021 Apr;27:1-34. link
Jalal A, Arvinte M, Daras G, Price E, Dimakis AG, Tamir J. Robust compressed sensing mri with deep generative priors. Advances in Neural Information Processing Systems. 2021 Dec 6;34:14938-54. link
Chen X, Derezinski M. Query complexity of least absolute deviation regression via robust uniform convergence. InConference on Learning Theory 2021 Jul 21 (pp. 1144-1179). PMLR. link
Charisopoulos V, Chen Y, Davis D, Díaz M, Ding L, Drusvyatskiy D. Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence. Foundations of Computational Mathematics. 2021 Dec;21(6):1505-93. link
Kim KS, Lee JH, Yang E. Compressed sensing via measurement-conditional generative models. IEEE Access. 2021 Nov 17;9:155335-52. link
Cui W, Liu S, Jiang F, Zhao D. Image compressed sensing using non-local neural network. IEEE Transactions on multimedia. 2021 Dec 3;25:816-30. link
Song J, Chen B, Zhang J. Memory-augmented deep unfolding network for compressive sensing. InProceedings of the 29th ACM international conference on multimedia 2021 Oct 17 (pp. 4249-4258). link
Zheng R, Zhang Y, Huang D, Chen Q. Sequential convolution and runge-kutta residual architecture for image compressed sensing. InComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 2020 (pp. 232-248). Springer International Publishing. link
Zhang Z, Liu Y, Liu J, Wen F, Zhu C. AMP-Net: Denoising-based deep unfolding for compressive image sensing. IEEE Transactions on Image Processing. 2020 Dec 18;30:1487-500. link
Kasem HM, Selim MM, Mohamed EM, Hussein AH. DRCS-SR: Deep robust compressed sensing for single image super-resolution. IEEE Access. 2020 Sep 15;8:170618-34. link
Zhang J, Zhao C, Gao W. Optimization-inspired compact deep compressive sensing. IEEE Journal of Selected Topics in Signal Processing. 2020 Mar 2;14(4):765-74. link
Zhang J, Li Y, Yu ZL, Gu Z, Cheng Y, Gong H. Deep Unfolding With Weighted ℓ₂ Minimization for Compressive Sensing. IEEE Internet of Things Journal. 2020 Sep 4;8(4):3027-41. link
Qiao M, Meng Z, Ma J, Yuan X. Deep learning for video compressive sensing. Apl Photonics. 2020 Mar 1;5(3). link
Ran M, Xia W, Huang Y, Lu Z, Bao P, Liu Y, Sun H, Zhou J, Zhang Y. MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI. IEEE Transactions on Radiation and Plasma Medical Sciences. 2020 May 1;5(1):120-35. link
Huang B, Zhou J, Yan X, Jing ME, Wan R, Fan Y. Cs-mcnet: A video compressive sensing reconstruction network with interpretable motion compensation. InProceedings of the Asian Conference on Computer Vision 2020. link
Deora P, Vasudeva B, Bhattacharya S, Pradhan PM. Structure preserving compressive sensing MRI reconstruction using generative adversarial networks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops 2020 (pp. 522-523). link
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Polykovskiy D, Vetrov D. Deterministic decoding for discrete data in variational autoencoders. InInternational conference on artificial intelligence and statistics 2020 Jun 3 (pp. 3046-3056). PMLR. link
Liu L, Shen Y, Li T, Caramanis C. High dimensional robust sparse regression. InInternational Conference on Artificial Intelligence and Statistics 2020 Jun 3 (pp. 411-421). PMLR. link
Daskalakis C, Rohatgi D, Zampetakis E. Constant-expansion suffices for compressed sensing with generative priors. Advances in Neural Information Processing Systems. 2020;33:13917-26. link
Wang J, Huang J, Zhang F, Wang W. Group sparse recovery in impulsive noise via alternating direction method of multipliers. Applied and Computational Harmonic Analysis. 2020 Nov 1;49(3):831-62. link
Calderon D, Juba B, Li S, Li Z, Ruan L. Conditional linear regression. InInternational Conference on Artificial Intelligence and Statistics 2020 Jun 3 (pp. 2164-2173). PMLR. link
Macris N, Rush C. All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation. Advances in Neural Information Processing Systems. 2020;33:14915-26. link
Popilka B, Setzer S, Steidl G. Signal recovery from incomplete measurements in the presence of outliers. Inverse Problems and Imaging. 2007 Nov 1;1(4):661. link
Aberdam A, Simon D, Elad M. When and how can deep generative models be inverted?. arXiv preprint arXiv:2006.15555. 2020 Jun 28. link
Asim M, Daniels M, Leong O, Ahmed A, Hand P. Invertible generative models for inverse problems: mitigating representation error and dataset bias. InInternational conference on machine learning 2020 Nov 21 (pp. 399-409). PMLR. link
Cocola J, Hand P, Voroninski V. Nonasymptotic guarantees for spiked matrix recovery with generative priors. Advances in Neural Information Processing Systems. 2020;33:15185-97. link
Cherapanamjeri Y, Aras E, Tripuraneni N, Jordan MI, Flammarion N, Bartlett PL. Optimal robust linear regression in nearly linear time. arXiv preprint arXiv:2007.08137. 2020 Jul 16. link
Pandit P, Sahraee-Ardakan M, Rangan S, Schniter P, Fletcher AK. Inference with deep generative priors in high dimensions. IEEE Journal on Selected Areas in Information Theory. 2020 Apr 8;1(1):336-47. link
Koh PW, Nguyen T, Tang YS, Mussmann S, Pierson E, Kim B, Liang P. Concept bottleneck models. InInternational conference on machine learning 2020 Nov 21 (pp. 5338-5348). PMLR. link
Zeng C, Wu C, Jia R. Non-Lipschitz models for image restoration with impulse noise removal. SIAM Journal on Imaging Sciences. 2019;12(1):420-58. link
Shen Y, Sanghavi S. Learning with bad training data via iterative trimmed loss minimization. InInternational conference on machine learning 2019 May 24 (pp. 5739-5748). PMLR. link
Heckel R, Soltanolkotabi M. Denoising and regularization via exploiting the structural bias of convolutional generators. arXiv preprint arXiv:1910.14634. 2019 Oct 31. link
Latorre F, Cevher V. Fast and provable ADMM for learning with generative priors. Advances in Neural Information Processing Systems. 2019;32. link
Ma J, Liu XY, Shou Z, Yuan X. Deep tensor admm-net for snapshot compressive imaging. InProceedings of the IEEE/CVF International Conference on Computer Vision 2019 (pp. 10223-10232). link
Liu R, Zhang Y, Cheng S, Fan X, Luo Z. A theoretically guaranteed deep optimization framework for robust compressive sensing mri. InProceedings of the AAAI Conference on Artificial Intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 4368-4375). link
Shi W, Jiang F, Liu S, Zhao D. Image compressed sensing using convolutional neural network. IEEE Transactions on Image Processing. 2019 Jul 17;29:375-88. link
Shi W, Jiang F, Liu S, Zhao D. Scalable convolutional neural network for image compressed sensing. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 (pp. 12290-12299). link
Canh TN, Jeon B. Difference of convolution for deep compressive sensing. In2019 IEEE International Conference on Image Processing (ICIP) 2019 Sep 22 (pp. 2105-2109). IEEE. link
Yang Y, Sun J, Li H, Xu Z. ADMM-CSNet: A deep learning approach for image compressive sensing. IEEE transactions on pattern analysis and machine intelligence. 2018 Nov 28;42(3):521-38. link
Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, Knoll F. Learning a variational network for reconstruction of accelerated MRI data. Magnetic resonance in medicine. 2018 Jun;79(6):3055-71. link
Kabkab M, Samangouei P, Chellappa R. Task-aware compressed sensing with generative adversarial networks. InProceedings of the AAAI conference on artificial intelligence 2018 Apr 26 (Vol. 32, No. 1). link
Seitzer M, Yang G, Schlemper J, Oktay O, Würfl T, Christlein V, Wong T, Mohiaddin R, Firmin D, Keegan J, Rueckert D. Adversarial and perceptual refinement for compressed sensing MRI reconstruction. InInternational conference on medical image computing and computer-assisted intervention 2018 Sep 16 (pp. 232-240). Cham: Springer International Publishing. link
Iliadis M, Spinoulas L, Katsaggelos AK. Deep fully-connected networks for video compressive sensing. Digital Signal Processing. 2018 Jan 1;72:9-18. link
Cui W, Xu H, Gao X, Zhang S, Jiang F, Zhao D. An efficient deep convolutional Laplacian pyramid architecture for CS reconstruction at low sampling ratios. In2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 Apr 15 (pp. 1748-1752). IEEE. link
Du J, Xie X, Wang C, Shi G. Perceptual compressive sensing. InPattern Recognition and Computer Vision: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part III 1 2018 (pp. 268-279). Springer International Publishing. link
Zhang J, Ghanem B. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing. InProceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 1828-1837). link
Dhar M, Grover A, Ermon S. Modeling sparse deviations for compressed sensing using generative models. InInternational Conference on Machine Learning 2018 Jul 3 (pp. 1214-1223). PMLR. link
Shah V, Hegde C. Solving linear inverse problems using gan priors: An algorithm with provable guarantees. In2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) 2018 Apr 15 (pp. 4609-4613). IEEE. link
Vaswani N, Narayanamurthy P. Static and dynamic robust PCA and matrix completion: A review. Proceedings of the IEEE. 2018 Aug 6;106(8):1359-79. link
Hand P, Voroninski V. Global guarantees for enforcing deep generative priors by empirical risk. InConference On Learning Theory 2018 Jul 3 (pp. 970-978). PMLR. link
Hand P, Voroninski V. Global guarantees for enforcing deep generative priors by empirical risk. InConference On Learning Theory 2018 Jul 3 (pp. 970-978). PMLR. link
Zhang X, Wang L, Gu Q. A nonconvex free lunch for low-rank plus sparse matrix recovery. arXiv preprint arXiv:1702.06525. 2017 Feb. link
Cherapanamjeri Y, Gupta K, Jain P. Nearly optimal robust matrix completion. InInternational Conference on Machine Learning 2017 Jul 17 (pp. 797-805). PMLR. link
Wan Q, Duan H, Fang J, Li H, Xing Z. Robust Bayesian compressed sensing with outliers. Signal Processing. 2017 Nov 1;140:104-9. link
Bora A, Jalal A, Price E, Dimakis AG. Compressed sensing using generative models. InInternational conference on machine learning 2017 Jul 17 (pp. 537-546). PMLR. link
Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, Liu F, Arridge S, Keegan J, Guo Y, Firmin D. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE transactions on medical imaging. 2017 Dec 21;37(6):1310-21. link
Liu Q, Leung H. Synthesis-analysis deconvolutional network for compressed sensing. In2017 IEEE International Conference on Image Processing (ICIP) 2017 Sep 17 (pp. 1940-1944). IEEE. link
Metzler C, Mousavi A, Baraniuk R. Learned D-AMP: Principled neural network based compressive image recovery. Advances in neural information processing systems. 2017;30. link
Perdios D, Besson A, Arditi M, Thiran JP. A deep learning approach to ultrasound image recovery. In2017 IEEE international ultrasonics symposium (IUS) 2017 Sep 6 (pp. 1-4). Ieee. link
Mousavi A, Baraniuk RG. Learning to invert: Signal recovery via deep convolutional networks. In2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) 2017 Mar 5 (pp. 2272-2276). IEEE. link
Kulkarni K, Lohit S, Turaga P, Kerviche R, Ashok A. Reconnet: Non-iterative reconstruction of images from compressively sensed measurements. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 449-458). link
Palangi H, Ward R, Deng L. Distributed compressive sensing: A deep learning approach. IEEE Transactions on Signal Processing. 2016 Apr 21;64(17):4504-18. link
Xu K, Ren F. CSvideonet: A recurrent convolutional neural network for compressive sensing video reconstruction. arXiv preprint arXiv:1612.05203. 2016 Dec. link
Mu C, Zhang Y, Wright J, Goldfarb D. Scalable robust matrix recovery: Frank--Wolfe meets proximal methods. SIAM Journal on Scientific Computing. 2016;38(5):A3291-317. link
Wipf D. Non-convex rank minimization via an empirical Bayesian approach. arXiv preprint arXiv:1408.2054. 2014 Aug 9. link
Foygel R, Mackey L. Corrupted sensing: Novel guarantees for separating structured signals. IEEE Transactions on Information Theory. 2014 Jan 14;60(2):1223-47. link
Chen Y, Jalali A, Sanghavi S, Caramanis C. Low-rank matrix recovery from errors and erasures. IEEE Transactions on Information Theory. 2013 Mar 7;59(7):4324-37. link
Mitra K, Veeraraghavan A, Chellappa R. Analysis of sparse regularization based robust regression approaches. IEEE Transactions on Signal Processing. 2012 Nov 27;61(5):1249-57. link
Studer C, Kuppinger P, Pope G, Bolcskei H. Recovery of sparsely corrupted signals. IEEE Transactions on Information Theory. 2011 Dec 14;58(5):3115-30. link
Mahoney MW. Randomized algorithms for matrices and data. Foundations and Trends® in Machine Learning. 2011 Nov 21;3(2):123-224. link
Candès EJ, Li X, Ma Y, Wright J. Robust principal component analysis?. Journal of the ACM (JACM). 2011 Jun 9;58(3):1-37. link
Xu H, Caramanis C, Sanghavi S. Robust PCA via outlier pursuit. Advances in neural information processing systems. 2010;23. link
Wright J, Ma Y. Dense error correction via $\ell^ 1$-minimization. IEEE Transactions on Information Theory. 2010 Jun 14;56(7):3540-60. link
Lin Z, Chen M, Ma Y. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055. 2010 Sep 26. link
Carrillo RE, Barner KE, Aysal TC. Robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. IEEE Journal of Selected Topics in Signal Processing. 2010 Feb 22;4(2):392-408. link
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Lin Z, Ganesh A, Wright J, Wu L, Chen M, Ma Y. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Coordinated Science Laboratory Report no. UILU-ENG-09-2214, DC-246. 2009. link

Spectral Compressed Sensing Fundamentals and Algorithms

Phan DN, Gillis N. An inertial block majorization minimization framework for nonsmooth nonconvex optimization. Journal of Machine Learning Research. 2023;24(18):1-41. link
Cai JF, Choi JK, Li J, Wei K. Image restoration: structured low rank matrix framework for piecewise smooth functions and beyond. Applied and Computational Harmonic Analysis. 2022 Jan 1;56:26-60. link
Liu P, Zhang H. A theory of computational resolution limit for line spectral estimation. IEEE Transactions on Information Theory. 2021 Apr 22;67(7):4812-27. link
Chen Y, Chi Y, Fan J, Ma C. Spectral methods for data science: A statistical perspective. Foundations and Trends® in Machine Learning. 2021 Oct 20;14(5):566-806. link
Gribonval R, Nikolova M. On Bayesian estimation and proximity operators. Applied and Computational Harmonic Analysis. 2021 Jan 1;50:49-72. link
Chen J, Gao W, Wei K. Exact matrix completion based on low rank Hankel structure in the Fourier domain. Applied and Computational Harmonic Analysis. 2021 Nov 1;55:149-84. link
Li Y, Chi Y, Zhang H, Liang Y. Non-convex low-rank matrix recovery with arbitrary outliers via median-truncated gradient descent. Information and Inference: A Journal of the IMA. 2020 Jun;9(2):289-325. link
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Low-rank Matrix Recovery Fundamentals and Algorithms

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