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The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of the vector.
M. X. Goemans and D. P. Williamson, “Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming,” J. ACM , vol. 42, no. 6, pp. 1115–1145, 1995
1995
Earlier work this paper cites.
R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. Royal Stat. Soc. Series B , pp. 267–288, 1996
1996
Earlier work this paper cites.
G. G. Lorentz, M. von Golitschek, and Y. Makovoz, Constructive approximation: Advanced problems . Springer, 1996, vol. 304
1996
Earlier work this paper cites.
N. Alon and J. H. Spencer, The probabilistic method . John Wiley & Sons, 2004
2004
Earlier work this paper cites.
S. S. Vempala, The random projection method . American Mathematical Soc., 2005, vol. 65
2005
Earlier work this paper cites.
P. T. Boufounos and R. G. Baraniuk, “1-bit compressive sensing,” in Conf. Inf. Sci. Syst. (CISS) . IEEE, 2008, pp. 16–21
2008
Earlier work this paper cites.
M. Wainwright, “Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso),” IEEE Trans. Inf. Theory , vol. 55, no. 5, pp. 2183–2202, May 2009
2009
Earlier work this paper cites.
M. Wainwright, “Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting,” IEEE Trans. Inf. Theory , vol. 55, no. 12, pp. 5728–5741, Dec. 2009
2009
Earlier work this paper cites.
P. T. Boufounos, “Greedy sparse signal reconstruction from sign measurements,” in Conf. Rec. Asilomar. Conf. Sig. Syst. Comput. (ACSSC) . IEEE, 2009, pp. 1305–1309
2009
Earlier work this paper cites.
A. Zymnis, S. Boyd, and E. Candes, “Compressed sensing with quantized measurements,” IEEE Sig. Proc. Lett. , vol. 17, no. 2, pp. 149–152, 2009
2009
Earlier work this paper cites.
A. Gupta, R. Nowak, and B. Recht, “Sample complexity for 1-bit compressed sensing and sparse classification,” in Int. Symp. Inf. Theory (ISIT) . IEEE, 2010, pp. 1553–1557
2010
Earlier work this paper cites.
P. T. Boufounos, “Reconstruction of sparse signals from distorted randomized measurements,” in Int. Conf. Acoust. Sp. Sig. Proc. (ICASSP) , 2010, pp. 3998–4001
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
J. N. Laska, Z. Wen, W. Yin, and R. G. Baraniuk, “Trust, but verify: Fast and accurate signal recovery from 1-bit compressive measurements,” IEEE Trans. Sig. Proc. , vol. 59, no. 11, pp. 5289–5301, 2011
2011
Earlier work this paper cites.
Y. Plan and R. Vershynin, “Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach,” IEEE Trans. Inf. Theory , vol. 59, no. 1, pp. 482–494, 2012
2012
Earlier work this paper cites.
S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing . Springer New York, 2013
2013
Earlier work this paper cites.
D. L. Donoho, A. Javanmard, and A. Montanari, “Information-theoretically optimal compressed sensing via spatial coupling and approximate message passing,” IEEE Trans. Inf. Theory , vol. 59, no. 11, pp. 7434–7464, Nov. 2013
2013
Cited alongside, same era.
E. Arias-Castro, E. J. Candes, and M. A. Davenport, “On the fundamental limits of adaptive sensing,” IEEE Trans. Inf. Theory , vol. 59, no. 1, pp. 472–481, Jan. 2013
2013
Cited alongside, same era.
E. J. Candes and M. A. Davenport, “How well can we estimate a sparse vector?” Appl. Comp. Harm. Analysis , vol. 34, no. 2, pp. 317–323, 2013
2013
Cited alongside, same era.
S. Gopi, P. Netrapalli, P. Jain, and A. Nori, “One-bit compressed sensing: Provable support and vector recovery,” in Int. Conf. Mach. Learn. (ICML) , 2013, pp. 154–162
2013
Cited alongside, same era.
——, “One-bit compressed sensing by linear programming,” Comm. Pure Appl. Math. , vol. 66, no. 8, pp. 1275–1297, 2013
A. Bora, A. Jalal, E. Price, and A. G. Dimakis, “Compressed sensing using generative models,” in Int. Conf. Mach. Learn. (ICML) , 2017, pp. 537–546
2017
Later among the works it cites.
J. Acharya, A. Bhattacharyya, and P. Kamath, “Improved bounds for universal one-bit compressive sensing,” in Int. Symp. Inf. Theory (ISIT) , 2017, pp. 2353–2357
2017
Later among the works it cites.
L. Jacques and V. Cambareri, “Time for dithering: Fast and quantized random embeddings via the restricted isometry property,” Inf. Inference , vol. 6, no. 4, pp. 441–476, 2017
2017
Later among the works it cites.
2018
Later among the works it cites.
Z. Li, W. Xu, X. Zhang, and J. Lin, “A survey on one-bit compressed sensing: Theory and applications,” Front. Comput. Sci. , vol. 12, no. 2, pp. 217–230, 2018
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2013
Cited alongside, same era.
L. Jacques, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors,” IEEE Trans. Inf. Theory , vol. 59, no. 4, pp. 2082–2102, 2013
2013
Cited alongside, same era.
L. Jacques and C. D. Vleeschouwer, “Quantized iterative hard thresholding: Bridging 1-bit and high-resolution quantized compressed sensing,” in Int. Conf. Samp. Theory Apps. (SampTA) , 2013
2013
Cited alongside, same era.
D. Amelunxen, M. Lotz, M. B. McCoy, and J. A. Tropp, “Living on the edge: Phase transitions in convex programs with random data,” Information and Inference , vol. 3, no. 3, pp. 224–294, 2014
2014
Cited alongside, same era.
L. Zhang, J. Yi, and R. Jin, “Efficient algorithms for robust one-bit compressive sensing,” in Int. Conf. Mach. Learn. (ICML) , 2014, pp. 820–828
2014
Cited alongside, same era.
A. Ai, A. Lapanowski, Y. Plan, and R. Vershynin, “One-bit compressed sensing with non-Gaussian measurements,” Linear Algebra Appl. , vol. 441, pp. 222–239, 2014
2014
Cited alongside, same era.
R. Zhu and Q. Gu, “Towards a lower sample complexity for robust one-bit compressed sensing,” in Int. Conf. Mach. Learn. (ICML) , 2015, pp. 739–747
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
V. Shah and C. Hegde, “Solving linear inverse problems using GAN priors: An algorithm with provable guarantees,” in IEEE Int. Conf. Acoust. Sp. Sig. Proc. (ICASSP) , 2018, pp. 4609–4613
2018
Later among the works it cites.
M. Dhar, A. Grover, and S. Ermon, “Modeling sparse deviations for compressed sensing using generative models,” in Int. Conf. Mach. Learn. (ICML) , 2018
2018
Later among the works it cites.
P. Hand and V. Voroninski, “Global guarantees for enforcing deep generative priors by empirical risk,” in Conf. Learn. Theory (COLT) , 2018
2018
Later among the works it cites.
M. J. Wainwright, High-dimensional statistics: A non-asymptotic viewpoint . Cambridge University Press, 2019, vol. 48
2019
Later among the works it cites.
D. Foster, Generative Deep Learning : Teaching Machines to Paint, Write, Compose and Play . O’Reilly Media, Inc, USA, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Flodin, V. Gandikota, and A. Mazumdar, “Superset technique for approximate recovery in one-bit compressed sensing,” in Conf. Neur. Inf. Proc. Sys. (NeurIPS) , 2019
2019
Later among the works it cites.
P. Peng, S. Jalali, and X. Yuan, “Solving inverse problems via auto-encoders,” IEEE J. Sel. Areas Inf. Theory , vol. 1, no. 1, pp. 312–323, 2020
2020
Closest in time.
Z. Liu and J. Scarlett, “Information-theoretic lower bounds for compressive sensing with generative models,” IEEE J. Sel. Areas Inf. Theory , vol. 1, no. 1, pp. 292–303, 2020
2020
Closest in time.