Publications also available via my Google Scholar profile.
2023
 A. Xu, A. D. McRae, J. Wang, M. A. Davenport, and A. Pananjady, “Perceptual adjustment queries and an inverted measurement paradigm for lowrank metric learning,” arxiv:2309.04626, September 2023.
 M. R. O'Shaughnessy, M. A. Davemport, and C. J. Rozell, “Distance preservation in statespace methods for detecting causal interations in dynamical systems,” arxiv:2308.06855, August 2023.
 M. R. O'Shaughnessy, D. S. Schiff, L. R. Varshney, C. J. Rozell, and M. A. Davenport, “What governs attitudes toward artificial intelligence adoption and governance?” Science and Public Policy, 50(2), pp. 161176, April 2023. [Ungated]
 A. D. McRae, J. Romberg, and M. A. Davenport,
“Optimal convex lifted sparse phase retrieval and PCA with an atomic matrix norm regularizer,”IEEE Trans. on Information Theory, 69(3), pp. 18661882, March 2023. [arXiv]
2022
 N. Ahad, M. A. Davenport, and Y. Xie,
“Dataadaptive symmetric CUSUM for sequential change detection,”
arxiv:2210.17353, October 2022.
 C. DeLude, R. S. Sharma, S. Karnik, C. Hood, M. A. Davenport, and J. Romberg,
“Iterative broadband source localization,”
arxiv:2210.11669, October 2022.
 P. Guan, J. Jin, J. Romberg, and M. A. Davenport,
“Loop unrolled shallow equilibrium regularizer (LUSER): A memoryefficient inverse problem solver,”
arxiv:2210.04987, October 2022.
 N. Ahad, S. E. Sonenblum, M. A. Davenport, and S. Sprigle,
“Validating a wheelchair inseat activity tracker.” Assistive Technology, 34(5), pp. 588598, October 2022. [Ungated]
 S. Karnik, J. Romberg, and M. A. Davenport,
“Thomson’s multitaper method revisited.” IEEE Trans. on Information Theory, 68(7), pp. 48644891, July 2022. [arXiv]
 C. DeLude, S. Karnik, M. A. Davenport, and J. Romberg,
“Broadband beamforming via linear embedding,”
arXiv:2206.07143, June 2022.
 A. D. McRae, A. Xu, J. Jin, N. Nadagouda, N. Ahad, P. Guan, S. Karnik, and M. A. Davenport,
“Delta distancing: A lifting approach to localizing items from user comparisons,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, May 2022.
 A. D. McRae, S. Karnik, M. A. Davenport, and V. Muthukumar,
“Harmless interpolation in regression and classification with structured features,”
in Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Online, March 2022.
 N. Ahad, E. L. Dyer, K. B. Hengen, Y. Xie, and M. A. Davenport,
“Learning Sinkhorn divergences for supervised change point detection,”
arXiv:2202.04000, February 2022.
 N. Nadagouda, A. Xu, and M. A. Davenport,
“Active metric learning and classification using similarity queries,”
arXiv:2202.01953, February 2022.
2021
 F. Zhu, A. R. Sedler, H. A. Grier, N. Ahad, M. A. Davenport, M. T. Kaufman, A. Giovannucci, and C. Pandarinath,
“Deep inference of latent dynamics with spatiotemporal superresolution using selective backpropagation through time,” in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Online, December 2021.
 S. Karnik, J. Romberg, and M. A. Davenport,
“Improved characterization of the eigenvalue behavior of discrete prolate spheroidal sequences.” Applied and Computational Harmonic Analysis, 55, pp. 97128, November 2021. [arXiv].
 A. K. Massimino and M. A. Davenport,
“As you like it: Localization via paired comparisons.” J. of Machine Learning Research, 22(186), pp. 139, 2021. [arXiv]
 N. Nadagouda and M. A. Davenport,
“Switched Hawkes processes,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Online, June 2021. [Ungated]
 A. D. McRae and M. A. Davenport,
“Lowrank matrix completion and denoising under Poisson noise.” Information and Inference, 10(2), pp. 697720, June 2021. [arXiv]
 N. Ahad and M. A. Davenport,
“Semisupervised sequence classification through change point detection.” in Proc. AAAI Conf. on Artificial Intelligenec (AAAI21), Online, January 2021.
2020
 A. Xu and M. A. Davenport,
“Simultaneous preference and metric learning from paired comparisons.” in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Online, December 2020. Spotlight Presentation
 A. D. McRae, J. Romberg, and M. A. Davenport,
“Sample complexity and effective dimension for regression on manifolds.” in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Online, December 2020.
 M. O'Shaughnessy, G. Canal, M. Connor, M. A. Davenport, and C. J. Rozell,
“Generative casual explanations of blackbox classifiers.” in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Online, December 2020.
 R. S. Srinivasa, M. A. Davenport, and J. Romberg,
“Sample complexity bounds for localized sketching,” in Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Online, August 2020.
 L. Xu and M. A. Davenport,
“Dynamic knowledge embedding and tracing,” in Proc. Educational Data Mining (EDM), Online, July 2020.
 G. Canal, M. Connor, J. Jin, N. Nadagouda, M. O’Shaughnessy, C. J. Rozell, and M. A. Davenport,
“The PICASSO algorithm for Bayesian localization via paired comparisons in a union of subspaces model,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Online, May 2020. [Ungated]
 R. S. Srinivasa, M. A. Davenport, and J. Romberg,
“Trading beams for bandwidth: Imaging with randomized beamforming.” SIAM J. on Imaging Sciences, 13(1), pp. 317350, 2020. [arXiv]
 M. R. O'Shaughnessy, M. A. Davenport, and C. J. Rozell,
“Sparse Bayesian learning with dynamic filtering for inference of timevarying sparse signals.” IEEE Trans. on Signal Processing, 68, pp. 388403, 2020. [arXiv]
 C. Sabillon, A. Rashidi, B. Samanta, M. A. Davenport, and D. V. Anderson,
“Audiobased Bayesian model for productivity estimation of cyclic construction activities,” J. of Computing in Civil Engineering, 34(1), pp. 04019048, January 2020.
2019
 G. H. Canal, M. R. O’Shaughnessy, C. J. Rozell, and M. A. Davenport,
“Joint estimation of trajectory and dynamics from paired comparisons,” in Proc. IEEE Int. Work. on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), Le Gosier, Guadeloupe, December 2019.
[Ungated]
 M. R. O’Shaughnessy, M. A. Davenport, and C. J. Rozell,
“Dynamical system implementations of Sparse Bayesian Learning,” in Proc. IEEE Int. Work. on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), Le Gosier, Guadeloupe, December 2019.
[Ungated]
 C.F. Cheng, A. Rashidi, M. A. Davenport, D. V. Anderson, and C. Sabillon,
“Evaluation of software and hardware settings for audiobased analysis of construction operations,” Int. J. of Civil Engineering, 17(9), pp. 14691480, September 2019.
[Ungated ]
 S. Karnik, J. Romberg, and M. A. Davenport,“Fast multitaper spectral estimation,”in Proc. Sampling Theory and its Applications (SampTA), Bordeaux, France, July 2019. Winner of Best Student Paper Award[Ungated]
 A. McRae and M. A. Davenport,“Lowrank matrix completion and denoising under Poisson noise,”in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 2019. Finalist for Best Student Paper Award
 M. R. O’Shaughnessy, M. A. Davenport, and C. J. Rozell,“Robust incorporation of signal predictions into the Sparse Bayesian Learning framework,”in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 2019.
 G. H. Canal, A. K. Massimino, M. A. Davenport, and C. J. Rozell,“Active embedding search via noisy paired comparisons,”in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 2019.
 S. Karnik, J. Romberg, and M. A. Davenport,“Bandlimited signal reconstruction from nonuniform samples,”in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 2019.
 G. H. Canal, A. K. Massimino, M. A. Davenport, and C. J. Rozell,
“Active embedding search via noisy paired comparisons,” in Proc. Int. Conf. on Machine Learning (ICML), Long Beach, California, June 2019.
 M. G. Moore and M. A. Davenport,
“Estimation of Poisson arrival processes under linear models.” IEEE Trans. on Information Theory, 65(6), pp. 35553564, June 2019.
[arXiv]
 S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport,
“The Fast Slepian Transform.” Applied and Computational Harmonic Analysis, 46(3), pp. 624652, May 2019.
[arXiv]
2018
 T. J. LaGrow, M. G. Moore, J. A. Prasad, A. Webber, M. A. Davenport, and E. L. Dyer,
“Sparse recovery methods for cell detection and layer estimation.” bioRxiv, doi:10.1101/445742, December 2018.

R. S. Srinivasa, M. A. Davenport, and J. Romberg,
“Localized random projections with applications to coherent array imaging,” in Proc. Allerton Conf. on Communication, Control, and Computing, Allerton, Illinois, October 2018.

Z. Zhu, S. Karnik, M. B. Wakin, M. A. Davenport, and J. Romberg,
“ROAST: Rapid Orthogonal Approximate Slepian Transform,” IEEE Trans. on Signal Processing, 66(22), pp. 58875901, November 2018.
[arXiv]
 H. Xu, L. Yu, M. A. Davenport, and H. Zha,
“A unified framework for manifold landmarking.” IEEE Trans. on Signal Processing, 66(21), pp. 55635576, November 2018.
[arXiv]
 T. J. LaGrow, M. G. Moore, J. A. Prasad, M. A. Davenport, and E. L. Dyer,
“Approximating cellular densities from highresolution neuroanatomical imaging data,” in Proc. IEEE Int. Engineering in Medicine and Biology Conf., Honolulu, Hawaii, July 2018.
 C.F. Cheng, D. V. Anderson, M. A. Davenport, and A. Rashidi,
“Audio classification on weakly labeled data,” in Proc. IEEE Work. on Statistical Signal Processing, Freiburg, Germany, June 2018.
 C. A. Sabillon, A. Rashidi, B. Samanta, C. F. Cheng, M. A. Davenport, and D. V. Anderson,
“A productivity forecasting system for construction cyclic operations using audio signals and a Bayesian approach,” in Proc. Construction Research Congress (CRC), New Orleans, Louisiana, April 2018.
 Z. Zhu, S. Karnik, M. A. Davenport, J. Romberg, and M. B. Wakin,
“The eigenvalue distribution of discrete periodic timefrequency limiting operators.” IEEE Signal Processing Letters, 25(1), pp. 9599, January 2018.
[arXiv]
2017
 L. Xu and M. A. Davenport,
“Simultaneous recovery of a series of lowrank matrices by locally weighted matrix smoothing,” in Proc. IEEE Int. Work. on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), Curaçao, Dutch Antilles, December 2017.
 A. J. Little, N. Ahad, M. A. Davenport, and S. Sonenblum,
“Towards a realtime inseat activity tracker,”
Black in AI Workshop (colocated with NeurIPS), Long Beach, California, December 2017.
 C.F. Cheng, A. Rashidi, M. A. Davenport, and D. V. Anderson,
“Activity analysis of construction equipment using audio signals and support vector machines” Automation in Construction, 81, pp. 240253, September 2017.
[Ungated]
 C. F. Cheng, A. Rashidi, M. A. Davenport, D. V. Anderson, and C. A. Sabillon,
“Hardware and software requirements for acoustical monitoring of construction jobsites,” in Proc. Int. Work. On Computing in Civil Engineering (IWCCE), Seattle, Washington, June 2017.
 A. Rashidi, M. A. Davenport, D. V. Anderson, C. F. Cheng, and C. A. Sabillon,
“Achievements and challenges in audiobased modeling of construction operations,” 173rd Meet. of the Acoustical Society of America, Boston, Massachusetts, June 2017.
 M. G. Moore and M. A. Davenport,
“Sparse parametric estimation of Poisson processes,” Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lisbon, Portugal, June 2017.
 L. Xu and M. A. Davenport,
“Dynamic onebit matrix completion,” Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lisbon, Portugal, June 2017.
 R. S. Srinivasa, M. A. Davenport, and J. Romberg,
“Sketching with structured matrices for array imaging,” Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lisbon, Portugal, June 2017.
 A. K. Massimino and M. A. Davenport,
“The geometry of random paired comparisons,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, Louisiana, March 2017.
[Ungated]
 Z. Zhu, S. Karnik, M. B. Wakin, M. A. Davenport, and J. Romberg,
“Fast orthogonal approximations of sampled sinusoids and bandlimited signals,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, Louisiana, March 2017.
[Ungated]
2016
 L. Xu and M. A. Davenport,
"Dynamic matrix recovery from incomplete observations under an exact lowrank constraint," in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Barcelona, Spain, December 2016.
[arXiv]
 S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport,
“Fast computations for approximation and compression in Slepian spaces,” in Proc. IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Washington, DC, December 2016.
[Ungated]
 M. A. Davenport, A. K. Massimino, D. Needell, and T. Woolf,
“Constrained adaptive sensing.” IEEE Trans. on Signal Processing, 64(20), pp. 54375449, October 2016.
[arXiv]
 M. R. O'Shaughnessy and M. A. Davenport,
“Localizing users and items from paired comparisons,” in Proc. IEEE Int. Work. on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Salerno, Italy, September 2016.
[Ungated]
 M. G. Moore and M. A. Davenport,
“Analysis of wireless networks using Hawkes processes,” in Proc. IEEE Int. Work. on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, Scotland, July 2016.
[Ungated]
 CF. Cheng, A. Rashidi, M. A. Davenport, and D. Anderson,
“Audio signal processing for activity recognition of construction heavy equipment,”
in Proc. Int. Symp. on Automation and Robotics in Construction (ISARC), Auburn, Alabama, July 2016.
 M. A. Davenport and J. Romberg,
“An overview of lowrank matrix recovery from incomplete observations,” IEEE J. of Selected Topics in Signal Processing, 10(4), pp. 608622, June 2016. [arXiv]
 A. K. Massimino and M. A. Davenport,
“Binary stable embedding via paired comparisons,” in Proc. IEEE Work. on Statistical Signal Processing (SSP), Palma de Mallorca, Spain, June 2016.
[Ungated]
 M. G. Moore and M. A. Davenport,
“A Hawkes’ eye view of network information flow,” in Proc. IEEE Work. on Statistical Signal Processing (SSP), Palma de Mallorca, Spain, June 2016.
[Ungated]
2015
 M. G. Moore, A. K. Massimino, and M. A. Davenport,
“Randomized multipulse timeofflight mass spectrometry,” in Proc. IEEE Int. Work. on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015.
[Ungated]
 M. A. Davenport, A. K. Massimino, D. Needell, and T. Woolf,
“Constrained adaptive sensing,” in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Cambridge, United Kingdom, July 2015.
 M. G. Moore and M. A. Davenport,
“Learning network structure via Hawkes processes,” in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Cambridge, United Kingdom, July 2015.
 H. Xu, H. Zha, and M. A. Davenport,
“Active manifold learning via Gershgorin circle guided sample selection,” in Proc. AAAI Conf. on Artificial Intelligence (AAAI15), Austin, Texas, January 2015.
2014
 M. A. Davenport, Y. Plan, E. van den Berg, and M. Wootters,
“1bit matrix completion,” Information and Inference, 3(3), pp. 189223, September 2014. Finalist for Information and Inference Best Paper Prize [arXiv]
 H. Xu, H. Zha, and M. A. Davenport,
“Manifold based dynamic texture synthesis from extremely few samples,” in Proc. IEEE Conf. on Computer Visition and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.
[Ungated]
 R. Willett, M. F. Duarte, M. A. Davenport, and R. G. Baraniuk,
"Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection," IEEE Signal Processing Magazine, 31(1), pp. 116126, January 2014.
[Ungated]
2013
 M. A. Davenport,
“Lost without a compass: Nonmetric triangulation and landmark multidimensional scaling,” in Proc. IEEE Int. Work. on Computational Advances in MultiSensor Adaptive Processing (CAMSAP), Saint Martin, December 2013.
[Ungated]
 A. Cohen, M. A. Davenport, and D. Leviatan,
"On the stability and accuracy of least squares approximations," Foundations of Computational Mathematics, 13(5), pp. 819834, October 2013.
[arXiv]
 M. A. Davenport, D. Needell, and M. B. Wakin,
"Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries," IEEE Trans. on Information Theory, 59(10), pp. 68206829, October 2013.
[arXiv]
 M. Wootters, Y. Plan, M. A. Davenport, and E. van den Berg,
“Lower bounds for quantized matrix completion,” in Proc. IEEE Int. Symp. on Information Theory (ISIT), Istanbul, Turkey, July 2013.
[Ungated]
 A. K. Massimino and M. A. Davenport,
“Onebit matrix completion for pairwise comparison matrices,” in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lausanne, Switzerland, July 2013.
 M. G. Moore and M. A. Davenport,
“Orthogonal matching pursuit with dictionary refinement for multitone signal recovery,” in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lausanne, Switzerland, July 2013.
 A. Charles, A. Ahmed, A. Joshi, S. Conover, C. Turnes, and M. A. Davenport,
“Cleaning up toxic waste: Removing nefarious contributions to recommendation systems,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013.
[Ungated]

E. J. Candès and M. A. Davenport,
"How well can we estimate a sparse vector?" Applied and Computational Harmonic Analysis, 34(2), pp. 317323, March 2013.
[arXiv]

E. AriasCastro, E. J. Candès, and M. A. Davenport,
"On the fundamental limits of adaptive sensing," IEEE Trans. on Information Theory, 59(1), pp. 472481, January 2013.
[arXiv]
2012
 M. A. Davenport, M. F. Duarte, Y. C. Eldar, and G. Kutyniok,
"Introduction to Compressed Sensing," in Compressed Sensing: Theory and Applications, Cambridge University Press, 2012.
[Amazon.com]
 M. A. Davenport and M. B. Wakin,
"Compressive sensing of analog signals using discrete prolate spheroidal sequences," Applied and Computational Harmonic Analysis, 33(3), pp. 438472, November 2012.
[arXiv]
 M. A. Davenport, D. Needell, and M. B. Wakin,
“CoSaMP with redundant dictionaries,” in Proc. 46th Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, California, November 2012.
[Ungated]

M. A. Davenport, J. N. Laska, J. R. Treichler, and R. G. Baraniuk,
"The pros and cons of compressive sensing for wideband signal acquisition: Noise folding vs. dynamic range," IEEE Trans. on Signal Processing, 60(9), September 2012.
[arXiv]

M. A. Davenport and E. AriasCastro,
"Compressive binary search," in Proc. IEEE Int. Symp. on Information Theory (ISIT), Cambridge, Massachusetts, July 2012.
[arXiv]
 S. R. Schnelle, J. P. Slavinsky, P. T. Boufounos, M. A. Davenport, and R. G. Baraniuk,
"A compressive phaselocked loop," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, March 2012.
[Ungated]
2011
 J. N. Laska, P. T. Boufounos, M. A. Davenport, and R. G. Baraniuk,
"Democracy in action: Quantization, saturation, and compressive sensing," Applied and Computational Harmonic Analysis, 31(3) pp. 429443, November 2011.
[Ungated]
 L. Xu, M. A. Davenport, M. A. Turner, T. Sun, K. F. Kelly,
"Compressive Echelle spectroscopy," in Proc. Unconventional Imaging and Wavefront Sensing VII at SPIE Optics & Photonics, San Diego, California, August 2011.
[Ungated]
 J. R. Treichler, M. A. Davenport, J. N. Laska, and R. G. Baraniuk,
"Dynamic range and compressive sensing acquisition receivers," in Proc. 7th U.S. / Australia Joint Work. on Defense Applications of Signal Processing (DASP), Coolum, Australia, July 2011.
 M. A. Davenport and M. B. Wakin,
"Reconstruction and cancellation of sampled multiband signals using discrete prolate spheroidal sequences," in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Edinburgh, Scotland, June 2011.
 J. P. Slavinsky, J. N. Laska, M. A. Davenport, and R. G. Baraniuk,
"The compressive multiplexer for multichannel compressive sensing," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 2011.
[Ungated]
2010
 M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk,
"Highdimensional data fusion via joint manifold learning," in Proc. AAAI Fall 2010 Symp. on Manifold Learning, Arlington, Virginia, November 2010.
 M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk,
"Joint manifolds for data fusion," IEEE Trans. on Image Processing, 19(10) pp. 25802594, October 2010.
[Ungated]
 M. A. Davenport, R. G. Baraniuk, and C. D. Scott,
"Tuning support vector machines for minimax and NeymanPearson classification," IEEE Trans. on Pattern Analysis and Machine Intelligence, 32(10) pp. 18881898, October 2010.
[Ungated]
 M. A. Davenport, S. R. Schnelle, J. P. Slavinsky, R. G. Baraniuk, M. B. Wakin, and P. T. Boufounos,
"A wideband compressive radio receiver," in Proc. Military Communications Conf. (MILCOM), San Jose, California, October 2010.
[Ungated]
 M. A. Davenport and M. B. Wakin,
"Analysis of orthogonal matching pursuit using the restricted isometry property," IEEE Trans. on Information Theory, 56(9) pp. 43954401, September 2010.
[arXiv]
 M. A. Davenport,
"Random observations on random observations: Sparse signal acquisition and processing," Ph.D. Thesis, Rice University, August 2010.
Winner of 2011 Ralph Budd Award from Rice University for best thesis in the School of Engineering
 M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk,
"Signal processing with compressive measurements," IEEE J. of Selected Topics in Signal Processing, 4(2) pp. 445460, April 2010. Winner of IEEE Signal Processing Society Paper Award [Ungated]
 S. R. Schnelle, J. N. Laska, C. Hegde, M. F. Duarte, M. A. Davenport, and R. G. Baraniuk,
"Texas Hold 'Em algorithms for distributed compressive sensing," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, March 2010.
[Ungated]
2009
 M. A. Davenport and R. G. Baraniuk,
"Sparse geodesic paths," in Proc. AAAI Fall 2009 Symp. on Manifold Learning, Arlington, Virginia, November 2009.
 J. N. Laska, M. A. Davenport, and R. G. Baraniuk,
"Exact signal recovery from sparsely corrupted measurements through the pursuit of justice," in Proc. 43rd Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, California, November 2009.
[Ungated]
 M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk,
"A simple proof that random matrices are democratic," Rice University ECE Technical Report TREE 0906, November 2009.
 J. R. Treichler, M. A. Davenport, and R. G. Baraniuk,
"Application of compressive sensing to the design of wideband signal acquisition receivers," in Proc. 6th U.S. / Australia Joint Work. on Defense Applications of Signal Processing (DASP), Lihue, Hawaii, September 2009.
 M. A. Davenport, P. T. Boufounos, and R. G. Baraniuk,
"Compressive domain interference cancellation," in Proc. Work. on Signal Processing with Adaptive Sparse Structured Representations (SPARS), SaintMalo, France, April 2009.
 M. A. Davenport, C. Hegde, M. F. Duarte, and R. G. Baraniuk,
"A theoretical analysis of joint manifolds," Rice University ECE Technical Report TREE 0901, January 2009.
2008
 R. G. Baraniuk, M. A. Davenport, R. A. DeVore, and M. B. Wakin,
"A simple proof of the restricted isometry property for random matrices," Constructive Approximation, 28(3) pp. 253263, December 2008.
[Ungated]
 M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk,
"Singlepixel imaging via compressive sampling," IEEE Signal Processing Magazine, 25(2) pp. 8391, March 2008.
[Ungated]
2007
 M. A. Davenport, C. Hegde, M. B. Wakin, and R. G. Baraniuk,
"Manifoldbased approaches for improved classification," NeurIPS Work. on Topology Learning, Whistler, Canada, December 2007.
 C. Hegde, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk,
"Efficient machine learning using random projections," NeurIPS Work. on Efficient Machine Learning, Whistler, Canada, December 2007.
 M. F. Duarte, M. A. Davenport, M. B. Wakin, J. N. Laska, D. Takhar, K. F. Kelly, and R. G. Baraniuk,
"Multiscale random projections for compressive classification," in Proc. IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007.
[Ungated]
 M. A. Davenport, R. G. Baraniuk, and C. D. Scott,
"Minimax support vector machines," in Proc. IEEE Work. on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007.
[Ungated]
 C. D. Scott and M. A. Davenport,
"Regression level set estimation via costsensitive classification," IEEE Trans. on Signal Processing, 55 (6) pp. 27522757, June 2007.
[Ungated]
 M. A. Davenport,
"Error control for support vector machines," M.S. Thesis, Rice University, April 2007.
 M. A. Davenport, M. F. Duarte, M. B. Wakin, J. N. Laska, D. Takhar, K. F. Kelly, and R. G. Baraniuk,
"The smashed filter for compressive classification and target recognition," in Proc. Computational Imaging V at SPIE Electronic Imaging, San Jose, California, January 2007.
[Ungated]
2006
 M. A. Davenport, R. G. Baraniuk, and M. B. Wakin,
"Scalable inference and recovery from compressive measurements," NeurIPS Work. on Novel Applications of Dimensionality Reduction, Whistler, Canada, December 2006.
 M. A. Davenport, M. B. Wakin, and R. G. Baraniuk,
"Detection and estimation with compressive measurements," Rice University ECE Technical Report TREE 0610, November 2006.
 M. A. Davenport, R. G. Baraniuk, and C. D. Scott,
"Learning minimum volume sets with support vector machines," in Proc. IEEE Int. Work. on Machine Learning for Signal Processing (MLSP), Maynooth, Ireland, September 2006.
[Ungated]
 M. A. Davenport, R. G. Baraniuk, and C. D. Scott,
"Controlling false alarms with support vector machines," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006.
[Ungated]
 M. F. Duarte, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk,
"Sparse signal detection from incoherent projections," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006.
[Ungated]