Controlling False Alarms With Support Vector Machines. - Mark A. Davenport; Richard G. Baraniuk; Clayton D. Scott (ICASSP (5), 2006)

Sparse Signal Detection from Incoherent Projections. - Marco F. Duarte; Mark A. Davenport; Michael B. Wakin; Richard G. Baraniuk (ICASSP (3), 2006)

The smashed filter for compressive classification and target recognition. - Mark A. Davenport; Marco F. Duarte; Michael B. Wakin; Jason N. Laska; Dharmpal Takhar; Kevin F. Kelly; Richard G. Baraniuk (Computational Imaging, 2007)

Multiscale Random Projections for Compressive Classification. - Marco F. Duarte; Mark A. Davenport; Michael B. Wakin; Jason N. Laska; Dharmpal Takhar; Kevin F. Kelly; Richard G. Baraniuk (ICIP (6), 2007)

Regression Level Set Estimation Via Cost-Sensitive Classification. - Clayton D. Scott; Mark A. Davenport (IEEE Trans. Signal Process., 2007)

Single-pixel imaging via compressive sampling. - Marco F. Duarte; Mark A. Davenport; Dharmpal Takhar; Jason N. Laska; Ting Sun; Kevin F. Kelly; Richard G. Baraniuk (IEEE Signal Process. Mag., 2008)

Sparse Geodesic Paths. - Mark A. Davenport; Richard G. Baraniuk (AAAI Fall Symposium: Manifold Learning and Its Applications, 2009)

A Theoretical Analysis of Joint Manifolds - Mark A. Davenport; Chinmay Hegde; Marco F. Duarte; Richard G. Baraniuk (CoRR, 2009)

A simple proof that random matrices are democratic - Mark A. Davenport; Jason N. Laska; Petros Boufounos; Richard G. Baraniuk (CoRR, 2009)

High Dimensional Data Fusion via Joint Manifold Learning. - Mark A. Davenport; Chinmay Hegde; Marco F. Duarte; Richard G. Baraniuk (AAAI Fall Symposium: Manifold Learning and Its Applications, 2010)

Texas Hold 'Em algorithms for distributed compressive sensing. - Stephen R. Schnelle; Jason N. Laska; Chinmay Hegde; Marco F. Duarte; Mark A. Davenport; Richard G. Baraniuk (ICASSP, 2010)

Signal Processing With Compressive Measurements. - Mark A. Davenport; Petros Boufounos; Michael B. Wakin; Richard G. Baraniuk (IEEE J. Sel. Top. Signal Process., 2010)

Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification. - Mark A. Davenport; Richard G. Baraniuk; Clayton D. Scott (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Joint Manifolds for Data Fusion. - Mark A. Davenport; Chinmay Hegde; Marco F. Duarte; Richard G. Baraniuk (IEEE Trans. Image Process., 2010)

Analysis of orthogonal matching pursuit using the restricted isometry property. - Mark A. Davenport; Michael B. Wakin (IEEE Trans. Inf. Theory, 2010)

The compressive multiplexer for multi-channel compressive sensing. - John P. Slavinsky; Jason N. Laska; Mark A. Davenport; Richard G. Baraniuk (ICASSP, 2011)

The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range - Mark A. Davenport; Jason N. Laska; John R. Treichler; Richard G. Baraniuk (CoRR, 2011)

How well can we estimate a sparse vector? - Emmanuel J. Candès; Mark A. Davenport (CoRR, 2011)

Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences - Mark A. Davenport; Michael B. Wakin (CoRR, 2011)

Introduction to compressed sensing. - Mark A. Davenport; Marco F. Duarte; Yonina C. Eldar; Gitta Kutyniok (Compressed Sensing, 2012)

CoSaMP with redundant dictionaries. - Mark A. Davenport; Deanna Needell; Michael B. Wakin (ACSCC, 2012)

A compressive phase-locked loop. - Stephen R. Schnelle; John P. Slavinsky; Petros Boufounos; Mark A. Davenport; Richard G. Baraniuk (ICASSP, 2012)

Compressive binary search. - Mark A. Davenport; Ery Arias-Castro (ISIT, 2012)

1-Bit Matrix Completion - Mark A. Davenport; Yaniv Plan; Ewout van den Berg; Mary Wootters (CoRR, 2012)

The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range. - Mark A. Davenport; Jason N. Laska; John R. Treichler; Richard G. Baraniuk (IEEE Trans. Signal Process., 2012)

Lost without a compass: Nonmetric triangulation and landmark multidimensional scaling. - Mark A. Davenport (CAMSAP, 2013)

Cleaning up toxic waste: Removing nefarious contributions to recommendation systems. - Adam Charles; Ali Ahmed 004; Aditya Joshi; Stephen Conover; Christopher K. Turnes; Mark A. Davenport (ICASSP, 2013)

Lower bounds for quantized matrix completion. - Mary Wootters; Yaniv Plan; Mark A. Davenport; Ewout van den Berg (ISIT, 2013)

On the Stability and Accuracy of Least Squares Approximations. - Albert Cohen 002; Mark A. Davenport; Dany Leviatan (Found. Comput. Math., 2013)

On the Fundamental Limits of Adaptive Sensing. - Ery Arias-Castro; Emmanuel J. Candès; Mark A. Davenport (IEEE Trans. Inf. Theory, 2013)

Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries. - Mark A. Davenport; Deanna Needell; Michael B. Wakin (IEEE Trans. Inf. Theory, 2013)

Manifold Based Dynamic Texture Synthesis from Extremely Few Samples. - Hongteng Xu; Hongyuan Zha; Mark A. Davenport (CVPR, 2014)

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection. - Rebecca M. Willett; Marco F. Duarte; Mark A. Davenport; Richard G. Baraniuk (IEEE Signal Process. Mag., 2014)

Active Manifold Learning via Gershgorin Circle Guided Sample Selection. - Hongteng Xu; Hongyuan Zha; Ren-Cang Li; Mark A. Davenport (AAAI, 2015)

Randomized multi-pulse time-of-flight mass spectrometry. - Michael G. Moore; Andrew K. Massimino; Mark A. Davenport (CAMSAP, 2015)

Fast computations for approximation and compression in Slepian spaces. - Santhosh Karnik; Zhihui Zhu; Michael B. Wakin; Justin K. Romberg; Mark A. Davenport (GlobalSIP, 2016)

Localizing users and items from paired comparisons. - Matthew R. O'Shaughnessy; Mark A. Davenport (MLSP, 2016)

Dynamic matrix recovery from incomplete observations under an exact low-rank constraint. - Liangbei Xu; Mark A. Davenport (NIPS, 2016)

Analysis of wireless networks using Hawkes processes. - Michael G. Moore; Mark A. Davenport (SPAWC, 2016)

Binary stable embedding via paired comparisons. - Andrew K. Massimino; Mark A. Davenport (SSP, 2016)

A Hawkes' eye view of network information flow. - Michael G. Moore; Mark A. Davenport (SSP, 2016)

An Overview of Low-Rank Matrix Recovery From Incomplete Observations. - Mark A. Davenport; Justin K. Romberg (IEEE J. Sel. Top. Signal Process., 2016)

Constrained Adaptive Sensing. - Mark A. Davenport; Andrew K. Massimino; Deanna Needell; Tina Woolf (IEEE Trans. Signal Process., 2016)

Simultaneous recovery of a series of low-rank matrices by locally weighted matrix smoothing. - Liangbei Xu; Mark A. Davenport (CAMSAP, 2017)

The geometry of random paired comparisons. - Andrew K. Massimino; Mark A. Davenport (ICASSP, 2017)

Fast orthogonal approximations of sampled sinusoids and bandlimited signals. - Zhihui Zhu; Santhosh Karnik; Michael B. Wakin; Mark A. Davenport; Justin K. Romberg (ICASSP, 2017)

Active manifold learning via a unified framework for manifold landmarking. - Hongteng Xu; Licheng Yu; Mark A. Davenport; Hongyuan Zha (CoRR, 2017)

Localized random projections with applications to coherent array imaging. - Rakshith Sharma Srinivasa; Mark A. Davenport; Justin Romberg (Allerton, 2018)

Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data. - Theodore J. LaGrow; Michael G. Moore; Judy A. Prasad; Mark A. Davenport; Eva L. Dyer (EMBC, 2018)

Audio Classification Based on Weakly Labeled Data. - Chieh-Feng Cheng; David V. Anderson; Mark A. Davenport; Abbas Rashidi (SSP, 2018)

The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators. - Zhihui Zhu; Santhosh Karnik; Mark A. Davenport; Justin Romberg; Michael B. Wakin (IEEE Signal Process. Lett., 2018)

A Unified Framework for Manifold Landmarking. - Hongteng Xu; Licheng Yu; Mark A. Davenport; Hongyuan Zha (IEEE Trans. Signal Process., 2018)

ROAST: Rapid Orthogonal Approximate Slepian Transform. - Zhihui Zhu; Santhosh Karnik; Michael B. Wakin; Mark A. Davenport; Justin Romberg (IEEE Trans. Signal Process., 2018)

Joint Estimation of Trajectory and Dynamics from Paired Comparisons. - Gregory H. Canal; Matthew R. O'Shaughnessy; Christopher J. Rozell; Mark A. Davenport (CAMSAP, 2019)

Dynamical System Implementations of Sparse Bayesian Learning. - Matthew R. O'Shaughnessy; Mark A. Davenport; Christopher J. Rozell (CAMSAP, 2019)

Active Embedding Search via Noisy Paired Comparisons. - Gregory Canal; Andrew K. Massimino; Mark A. Davenport; Christopher J. Rozell (ICML, 2019)

Low-rank matrix completion and denoising under Poisson noise. - Andrew D. McRae; Mark A. Davenport (CoRR, 2019)

Correction to: On the Stability and Accuracy of Least Squares Approximations. - Albert Cohen 002; Mark A. Davenport; Dany Leviatan (Found. Comput. Math., 2019)

Estimation of Poisson Arrival Processes Under Linear Models. - Michael G. Moore; Mark A. Davenport (IEEE Trans. Inf. Theory, 2019)

Sample complexity bounds for localized sketching. - Rakshith Sharma Srinivasa; Mark A. Davenport; Justin Romberg (AISTATS, 2020)

Dynamic Knowledge Embedding and Tracing. - Liangbei Xu; Mark A. Davenport (EDM, 2020)

The Picasso Algorithm for Bayesian Localization Via Paired Comparisons in a Union of Subspaces Model. - Gregory Canal; Marissa Connor; Jihui Jin; Namrata Nadagouda; Matthew R. O'Shaughnessy; Christopher J. Rozell; Mark A. Davenport (ICASSP, 2020)

Sample complexity and effective dimension for regression on manifolds. - Andrew D. McRae; Justin Romberg; Mark A. Davenport (NeurIPS, 2020)

Generative causal explanations of black-box classifiers. - Matthew R. O'Shaughnessy; Gregory Canal; Marissa Connor; Christopher Rozell; Mark A. Davenport (NeurIPS, 2020)

Simultaneous Preference and Metric Learning from Paired Comparisons. - Austin Xu; Mark A. Davenport (NeurIPS, 2020)

Localized sketching for matrix multiplication and ridge regression. - Rakshith Sharma Srinivasa; Mark A. Davenport; Justin Romberg (CoRR, 2020)

Audio-Based Bayesian Model for Productivity Estimation of Cyclic Construction Activities. - Chris Sabillon; Abbas Rashidi; Biswanath Samanta; Mark A. Davenport; David V. Anderson (J. Comput. Civ. Eng., 2020)

Trading Beams for Bandwidth: Imaging with Randomized Beamforming. - Rakshith Sharma Srinivasa; Mark A. Davenport; Justin Romberg (SIAM J. Imaging Sci., 2020)

Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals. - Matthew R. O'Shaughnessy; Mark A. Davenport; Christopher J. Rozell (IEEE Trans. Signal Process., 2020)

Semi-supervised Sequence Classification through Change Point Detection. - Nauman Ahad; Mark A. Davenport (AAAI, 2021)

Switched Hawkes Processes. - Namrata Nadagouda; Mark A. Davenport (ICASSP, 2021)

Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. - Feng Zhu; Andrew R. Sedler; Harrison A. Grier; Nauman Ahad; Mark A. Davenport; Matthew T. Kaufman; Andrea Giovannucci; Chethan Pandarinath (NeurIPS, 2021)

As You Like It: Localization via Paired Comparisons. - Andrew K. Massimino; Mark A. Davenport (J. Mach. Learn. Res., 2021)

Harmless interpolation in regression and classification with structured features. - Andrew D. McRae; Santhosh Karnik; Mark A. Davenport; Vidya K. Muthukumar (AISTATS, 2022)

Delta Distancing: A Lifting Approach to Localizing Items from User Comparisons. - Andrew D. McRae; Austin Xu; Jihui Jin; Namrata Nadagouda; Nauman Ahad; Peimeng Guan; Santhosh Karnik; Mark A. Davenport (ICASSP, 2022)

Learning Sinkhorn divergences for supervised change point detection. - Nauman Ahad; Eva L. Dyer; Keith B. Hengen; Yao Xie; Mark A. Davenport (CoRR, 2022)

Loop Unrolled Shallow Equilibrium Regularizer (LUSER) - A Memory-Efficient Inverse Problem Solver. - Peimeng Guan; Jihui Jin; Justin Romberg; Mark A. Davenport (CoRR, 2022)

Thomson's Multitaper Method Revisited. - Santhosh Karnik; Justin Romberg; Mark A. Davenport (IEEE Trans. Inf. Theory, 2022)

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning. - Austin Xu; Andrew D. McRae; Jingyan Wang 001; Mark A. Davenport; Ashwin Pananjady (NeurIPS, 2023)

Active metric learning and classification using similarity queries. - Namrata Nadagouda; Austin Xu; Mark A. Davenport (UAI, 2023)

Distance preservation in state-space methods for detecting causal interactions in dynamical systems. - Matthew R. O'Shaughnessy; Mark A. Davenport; Christopher Rozell (CoRR, 2023)

Iterative Broadband Source Localization. - Coleman DeLude; Rakshith Sharma Srinivasa; Santhosh Karnik; Christopher Hood; Mark A. Davenport; Justin Romberg (IEEE J. Sel. Areas Inf. Theory, 2023)

Optimal Convex Lifted Sparse Phase Retrieval and PCA With an Atomic Matrix Norm Regularizer. - Andrew D. McRae; Justin Romberg; Mark A. Davenport (IEEE Trans. Inf. Theory, 2023)

Stochastic Gradient Descent Jittering for Inverse Problems: Alleviating the Accuracy-Robustness Tradeoff. - Peimeng Guan; Mark A. Davenport (CoRR, 2024)

New Equivalences between Interpolation and SVMs: Kernels and Structured Features. - Chiraag Kaushik; Andrew D. McRae; Mark A. Davenport; Vidya Muthukumar (SIAM J. Math. Data Sci., 2024)

Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures. - Peimeng Guan; Naveed Iqbal 001; Mark A. Davenport; Mudassir Masood (Trans. Mach. Learn. Res., 2024)

Test-Time Forward Model Adaptation for Seismic Deconvolution. - Peimeng Guan; Naveed Iqbal 001; Mark A. Davenport; Mudassir Masood (IEEE Geosci. Remote. Sens. Lett., 2025)

Time Series Domain Adaptation via Channel-Selective Representation Alignment. - Nauman Ahad; Mark A. Davenport; Eva L. Dyer (Trans. Mach. Learn. Res., 2025)