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We consider a class of approximated message passing (AMP) algorithms and characterize their high-dimensional behavior in terms of a suitable state evolution recursion.
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S. Rangan, Generalized Approximate Message Passing for Estimation with Random Linear Mixing , IEEE Intl. Symp. on Inform. Theory (St. Petersbourg), August 2011, pp. 2168 – 2172
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D. Bean, P. Bickel, N. El Karoui, and B. Yu, Optimal objective function in high-dimensional regression , Submitted to PNAS (2012)
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A. Javanmard and A. Montanari, Subsampling at information theoretically optimal rates , IEEE Intl. Symp. on Inform. Theory (Cambridge), July 2012
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2011
Cited alongside, same era.
2011
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D.L. Donoho, A. Maleki, and A. Montanari, The Noise Sensitivity Phase Transition in Compressed Sensing , IEEE Trans. on Inform. Theory 57
2011
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A.K. Fletcher, S. Rangan, L.R. Varshney, and A. Bhargava, Neural reconstruction with approximate message passing (neuramp) , Proc. 25th Ann. Conf. Neural Information Processing Systems, NIPS, 2011
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2012
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U.S. Kamilov, A. Bourquard, A. Amini, and M. Unser, One-bit measurements with adaptive thresholds , Signal Processing Letters, IEEE 19
2012
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F. Krzakala, M. Mézard, F. Sausset, Y. Sun, and L. Zdeborová, Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices , Journal of Statistical Mechanics: Theory and Experiment 2012
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F. Krzakala, M. Mézard, F. Sausset, YF Sun, and L. Zdeborová, Statistical-physics-based reconstruction in compressed sensing , Physical Review X 2
2012
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S. Som and P. Schniter, Compressive imaging using approximate message passing and a markov-tree prior , Signal Processing, IEEE Transactions on 60
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