Christoffel Adaptive Sampling for Sparse Random Feature Expansions.

Ben Adcock

Ben Adcock Khiem Can Xuemeng Wang Christoffel Adaptive Sampling for Sparse Random Feature Expansions. 2026 March abs/2603.18251 CoRR https://doi.org/10.48550/arXiv.2603.18251 db/journals/corr/corr2603.html#abs-2603-18251 streams/journals/corr

Ben Adcock Avi Gupta Universal, sample-optimal algorithms for recovery of anisotropic functions from i.i.d. samples. 2026 April abs/2604.07660 CoRR https://doi.org/10.48550/arXiv.2604.07660 db/journals/corr/corr2604.html#abs-2604-07660 streams/journals/corr

Ben Adcock Optimal sampling for least-squares approximation. 1975-2034 2025 December 25 Found. Comput. Math. 6 https://doi.org/10.1007/s10208-025-09738-2 db/journals/focm/focm25.html#Adcock25 streams/journals/focm

Ben Adcock Nick C. Dexter Sebastian Moraga Optimal approximation of infinite-dimensional holomorphic functions II: Recovery from i.i.d. pointwise samples. 101933 2025 89 J. Complex. https://doi.org/10.1016/j.jco.2025.101933 db/journals/jc/jc89.html#AdcockDM25 streams/journals/jc

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. 106761 2025 181 Neural Networks https://doi.org/10.1016/j.neunet.2024.106761 db/journals/nn/nn181.html#AdcockBDM25 streams/journals/nn

Nina Maria Gottschling Vegard Antun Anders C. Hansen Ben Adcock The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems. 73-104 2025 67 SIAM Rev. 1 https://doi.org/10.1137/23m1568739 db/journals/siamrev/siamrev67.html#GottschlingAHA25 streams/journals/siamrev

Ben Adcock Function recovery and optimal sampling in the presence of nonuniform evaluation costs. 2025 February abs/2502.10613 CoRR https://doi.org/10.48550/arXiv.2502.10613 db/journals/corr/corr2502.html#abs-2502-10613 streams/journals/corr

Ben Adcock Nick Huang How many measurements are enough? Bayesian recovery in inverse problems with general distributions. 2025 May abs/2505.10630 CoRR https://doi.org/10.48550/arXiv.2505.10630 db/journals/corr/corr2505.html#abs-2505-10630 streams/journals/corr

Ben Adcock Bernhard Hientzsch Akil Narayan 0001 Yiming Xu 0009 Hybrid least squares for learning functions from highly noisy data. 2025 July abs/2507.02215 CoRR https://doi.org/10.48550/arXiv.2507.02215 db/journals/corr/corr2507.html#abs-2507-02215 streams/journals/corr

Astird Herremans Ben Adcock Refinement-based Christoffel sampling for least squares approximation in non-orthogonal bases. 2025 October abs/2510.08461 CoRR https://doi.org/10.48550/arXiv.2510.08461 db/journals/corr/corr2510.html#abs-2510-08461 streams/journals/corr

Ben Adcock Gregor Maier Rahul Parhi Towards Sharp Minimax Risk Bounds for Operator Learning. 2025 December abs/2512.17805 CoRR https://doi.org/10.48550/arXiv.2512.17805 db/journals/corr/corr2512.html#abs-2512-17805 streams/journals/corr

Ben Adcock Daan Huybrechs Cécile Piret Stable and Accurate Least Squares Radial Basis Function Approximations on Bounded Domains. 2698-2718 2024 62 SIAM J. Numer. Anal. 6 https://doi.org/10.1137/23m1593243 db/journals/siamnum/siamnum62.html#AdcockHP24 streams/journals/siamnum

Ben Adcock Juan M. Cardenas Nick C. Dexter A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. 2024 ICML https://proceedings.mlr.press/v235/adcock24a.html https://openreview.net/forum?id=wG2SgnH6Zv conf/icml/2024 db/conf/icml/icml2024.html#AdcockCD24 169-202 Ben Adcock Nick C. Dexter Sebastian Moraga Scheuermann Optimal deep learning of holomorphic operators between Banach spaces. 2024 NeurIPS http://papers.nips.cc/paper_files/paper/2024/hash/3100d29d662360bb1a40a5ded8e100ae-Abstract-Conference.html conf/nips/2024 db/conf/nips/neurips2024.html#AdcockDS24

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. 2024 abs/2404.03761 CoRR https://doi.org/10.48550/arXiv.2404.03761 db/journals/corr/corr2404.html#abs-2404-03761

Ben Adcock Nick C. Dexter Sebastian Moraga Optimal deep learning of holomorphic operators between Banach spaces. 2024 abs/2406.13928 CoRR https://doi.org/10.48550/arXiv.2406.13928 db/journals/corr/corr2406.html#abs-2406-13928

Alexander Bastounis Paolo Campodonico Mihaela van der Schaar Ben Adcock Anders C. Hansen On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'. 2024 abs/2408.02357 CoRR https://doi.org/10.48550/arXiv.2408.02357 db/journals/corr/corr2408.html#abs-2408-02357 streams/journals/corr

Ben Adcock Optimal sampling for least-squares approximation. 2024 abs/2409.02342 CoRR https://doi.org/10.48550/arXiv.2409.02342 db/journals/corr/corr2409.html#abs-2409-02342 streams/journals/corr

Ben Adcock Michael Griebel Gregor Maier Learning Lipschitz Operators with respect to Gaussian Measures with Near-Optimal Sample Complexity. 2024 abs/2410.23440 CoRR https://doi.org/10.48550/arXiv.2410.23440 db/journals/corr/corr2410.html#abs-2410-23440 streams/journals/corr

Ben Adcock Juan M. Cardenas Nick C. Dexter An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. 200- 2023 February 45 SIAM J. Sci. Comput. 1 https://doi.org/10.1137/22m1472693 db/journals/siamsc/siamsc45.html#AdcockCD23

Juan M. Cardenas Ben Adcock Nick C. Dexter CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. 2023 NeurIPS http://papers.nips.cc/paper_files/paper/2023/hash/3f8c7eb848ffec848f3ed2b7ca44915d-Abstract-Conference.html conf/nips/2023 db/conf/nips/neurips2023.html#CardenasAD23

Ben Adcock Matthew J. Colbrook Maksym Neyra-Nesterenko Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods. 2023 abs/2301.02268 CoRR https://doi.org/10.48550/arXiv.2301.02268 db/journals/corr/corr2301.html#abs-2301-02268

Ben Adcock Nick C. Dexter Sebastian Moraga Optimal approximation of infinite-dimensional holomorphic functions. 2023 abs/2305.18642 CoRR https://doi.org/10.48550/arXiv.2305.18642 db/journals/corr/corr2305.html#abs-2305-18642

Ben Adcock Juan M. Cardenas Nick C. Dexter CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. 2023 abs/2306.00945 CoRR https://doi.org/10.48550/arXiv.2306.00945 db/journals/corr/corr2306.html#abs-2306-00945

Ben Adcock Nick C. Dexter Sebastian Moraga Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples. 2023 abs/2310.16940 CoRR https://doi.org/10.48550/arXiv.2310.16940 db/journals/corr/corr2310.html#abs-2310-16940

Ben Adcock Juan M. Cardenas Nick C. Dexter A unified framework for learning with nonlinear model classes from arbitrary linear samples. 2023 abs/2311.14886 CoRR https://doi.org/10.48550/arXiv.2311.14886 db/journals/corr/corr2311.html#abs-2311-14886

Ben Adcock Simone Brugiapaglia Matthew King-Roskamp Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. 99-159 2022 22 Found. Comput. Math. 1 https://doi.org/10.1007/s10208-021-09501-3 db/journals/focm/focm22.html#AdcockBK22

Ben Adcock Juan M. Cardenas Nick C. Dexter An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains. 2022 abs/2202.00144 CoRR https://arxiv.org/abs/2202.00144 db/journals/corr/corr2202.html#abs-2202-00144

Ben Adcock Juan M. Cardenas Nick C. Dexter Sebastian Moraga Towards optimal sampling for learning sparse approximation in high dimensions. 2022 abs/2202.02360 CoRR https://arxiv.org/abs/2202.02360 db/journals/corr/corr2202.html#abs-2202-02360

Maksym Neyra-Nesterenko Ben Adcock Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models. 2022 abs/2203.00804 CoRR https://doi.org/10.48550/arXiv.2203.00804 db/journals/corr/corr2203.html#abs-2203-00804

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. 2022 abs/2203.13908 CoRR https://doi.org/10.48550/arXiv.2203.13908 db/journals/corr/corr2203.html#abs-2203-13908

Ben Adcock Simone Brugiapaglia Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? 2022 abs/2208.09045 CoRR https://doi.org/10.48550/arXiv.2208.09045 db/journals/corr/corr2208.html#abs-2208-09045

Ben Adcock Juan M. Cardenas Nick C. Dexter CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. 2022 abs/2208.12190 CoRR https://doi.org/10.48550/arXiv.2208.12190 db/journals/corr/corr2208.html#abs-2208-12190

Ben Adcock Daan Huybrechs Cécile Piret Stable and accurate least squares radial basis function approximations on bounded domains. 2022 abs/2211.12598 CoRR https://doi.org/10.48550/arXiv.2211.12598 db/journals/corr/corr2211.html#abs-2211-12598

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. 2022 abs/2211.12633 CoRR https://doi.org/10.48550/arXiv.2211.12633 db/journals/corr/corr2211.html#abs-2211-12633

Ben Adcock Mohsen Seifi Frame approximation with bounded coefficients. 4 2021 47 Adv. Comput. Math. 1 https://doi.org/10.1007/s10444-020-09820-z db/journals/adcm/adcm47.html#AdcockS21

Ben Adcock Nick C. Dexter Qinghong Xu Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. 1149-1183 2021 14 SIAM J. Imaging Sci. 3 https://doi.org/10.1137/20M136788X db/journals/siamis/siamis14.html#AdcockDX21

Ben Adcock Nick C. Dexter The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. 624-655 2021 3 SIAM J. Math. Data Sci. 2 https://doi.org/10.1137/20M131309X db/journals/simods/simods3.html#AdcockD21

Ben Adcock Simone Brugiapaglia Matthew King-Roskamp The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. 3160-3175 2021 69 IEEE Trans. Signal Process. https://doi.org/10.1109/TSP.2021.3080458 db/journals/tsp/tsp69.html#AdcockBK21

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. 2021 AAAI Spring Symposium: MLPS https://ceur-ws.org/Vol-2964/article_192.pdf conf/aaaiss/2021mlps db/conf/aaaiss/mlps2021.html#AdcockBDM21 Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. 1-36 2021 MSML https://proceedings.mlr.press/v145/adcock22a.html conf/msml/2021 db/conf/msml/msml2021.html#AdcockBDM21

Ben Adcock Alexei Shadrin On the possibility of fast stable approximation of analytic functions from equispaced samples via polynomial frames. 2021 abs/2110.03755 CoRR https://arxiv.org/abs/2110.03755 db/journals/corr/corr2110.html#abs-2110-03755

Ben Adcock Simone Brugiapaglia Matthew King-Roskamp Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. 2021 abs/2110.15420 CoRR https://arxiv.org/abs/2110.15420 db/journals/corr/corr2110.html#abs-2110-15420

Ben Adcock Juan M. Cardenas Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. 607-630 2020 2 SIAM J. Math. Data Sci. 3 https://doi.org/10.1137/19M1279459 db/journals/simods/simods2.html#AdcockC20

Ben Adcock Mohsen Seifi Frame approximation with bounded coefficients. 2020 abs/2001.00983 CoRR http://arxiv.org/abs/2001.00983 db/journals/corr/corr2001.html#abs-2001-00983

Nina Maria Gottschling Vegard Antun Ben Adcock Anders C. Hansen The troublesome kernel: why deep learning for inverse problems is typically unstable. 2020 abs/2001.01258 CoRR http://arxiv.org/abs/2001.01258 db/journals/corr/corr2001.html#abs-2001-01258

Ben Adcock Nick C. Dexter The gap between theory and practice in function approximation with deep neural networks. 2020 abs/2001.07523 CoRR https://arxiv.org/abs/2001.07523 db/journals/corr/corr2001.html#abs-2001-07523

Ben Adcock Simone Brugiapaglia Matthew King-Roskamp The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees. 2020 abs/2006.13389 CoRR https://arxiv.org/abs/2006.13389 db/journals/corr/corr2006.html#abs-2006-13389

Ben Adcock Nick C. Dexter Qinghong Xu Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging. 2020 abs/2009.08555 CoRR https://arxiv.org/abs/2009.08555 db/journals/corr/corr2009.html#abs-2009-08555

Ben Adcock Simone Brugiapaglia Nick C. Dexter Sebastian Moraga Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. 2020 abs/2012.06081 CoRR https://arxiv.org/abs/2012.06081 db/journals/corr/corr2012.html#abs-2012-06081

Ben Adcock Anyi Bao Simone Brugiapaglia Correcting for unknown errors in sparse high-dimensional function approximation. 667-711 2019 142 Numerische Mathematik 3 https://doi.org/10.1007/s00211-019-01051-9 https://www.wikidata.org/entity/Q127937698 db/journals/nm/nm142.html#AdcockBB19

Ben Adcock Daan Huybrechs Frames and Numerical Approximation. 443-473 2019 61 SIAM Rev. 3 https://doi.org/10.1137/17M1114697 https://www.wikidata.org/entity/Q127394459 db/journals/siamrev/siamrev61.html#AdcockH19

Ben Adcock Anne Gelb Guohui Song Yi Sui Joint Sparse Recovery Based on Variances. A246-A268 2019 41 SIAM J. Sci. Comput. 1 https://doi.org/10.1137/17M1155983 db/journals/siamsc/siamsc41.html#AdcockGSS19

Il Yong Chun David Hong Ben Adcock Jeffrey A. Fessler Convolutional Analysis Operator Learning: Dependence on Training Data. 1137-1141 2019 26 IEEE Signal Process. Lett. 8 https://doi.org/10.1109/LSP.2019.2921446 https://www.wikidata.org/entity/Q92097630 db/journals/spl/spl26.html#ChunHAF19

Vegard Antun Francesco Renna Clarice Poon Ben Adcock Anders C. Hansen On instabilities of deep learning in image reconstruction - Does AI come at a cost? 2019 abs/1902.05300 CoRR http://arxiv.org/abs/1902.05300 db/journals/corr/corr1902.html#abs-1902-05300

Il Yong Chun David Hong Ben Adcock Jeffrey A. Fessler Convolutional Analysis Operator Learning: Dependence on Training Data. 2019 abs/1902.08267 CoRR http://arxiv.org/abs/1902.08267 db/journals/corr/corr1902.html#abs-1902-08267

Ben Adcock Vegard Antun Anders C. Hansen Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. 2019 abs/1905.00126 CoRR http://arxiv.org/abs/1905.00126 db/journals/corr/corr1905.html#abs-1905-00126

Ben Adcock Simone Brugiapaglia Matthew King-Roskamp Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. 2019 abs/1905.10028 CoRR http://arxiv.org/abs/1905.10028 db/journals/corr/corr1905.html#abs-1905-10028

Ben Adcock Juan M. Cardenas Optimal sampling strategies for multivariate function approximation on general domains. 2019 abs/1908.01249 CoRR http://arxiv.org/abs/1908.01249 db/journals/corr/corr1908.html#abs-1908-01249

Ben Adcock Infinite-Dimensional Compressed Sensing and Function Interpolation. 661-701 2018 18 Found. Comput. Math. 3 https://doi.org/10.1007/s10208-017-9350-3 db/journals/focm/focm18.html#Adcock18

Ben Adcock Anyi Bao John D. Jakeman Akil Narayan 0001 Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. 1424-1453 2018 6 SIAM/ASA J. Uncertain. Quantification 4 https://doi.org/10.1137/17M112590X db/journals/juq/juq6.html#AdcockBJN18

Simone Brugiapaglia Ben Adcock Robustness to Unknown Error in Sparse Regularization. 6638-6661 2018 64 IEEE Trans. Inf. Theory 10 https://doi.org/10.1109/TIT.2017.2788445 db/journals/tit/tit64.html#BrugiapagliaA18

Ben Adcock Claire Boyer Simone Brugiapaglia On oracle-type local recovery guarantees in compressed sensing. 2018 abs/1806.03789 CoRR http://arxiv.org/abs/1806.03789 db/journals/corr/corr1806.html#abs-1806-03789

Ben Adcock Simone Brugiapaglia Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. 2018 abs/1810.11115 CoRR http://arxiv.org/abs/1810.11115 db/journals/corr/corr1810.html#abs-1810-11115

Ben Adcock Jesús Martín-Vaquero Mark Richardson Resolution-Optimal Exponential and Double-Exponential Transform Methods for Functions with Endpoint Singularities. 2017 39 SIAM J. Sci. Comput. 1 https://doi.org/10.1137/15M104517X https://www.wikidata.org/entity/Q115525642 db/journals/siamsc/siamsc39.html#AdcockMR17

Il Yong Chun Ben Adcock Compressed Sensing and Parallel Acquisition. 4860-4882 2017 63 IEEE Trans. Inf. Theory 8 https://doi.org/10.1109/TIT.2017.2700440 db/journals/tit/tit63.html#ChunA17

Simone Brugiapaglia Ben Adcock Robustness to unknown error in sparse regularization. 2017 abs/1705.10299 CoRR http://arxiv.org/abs/1705.10299 db/journals/corr/corr1705.html#BrugiapagliaA17

Ben Adcock Anders C. Hansen Generalized Sampling and Infinite-Dimensional Compressed Sensing. 1263-1323 2016 16 Found. Comput. Math. 5 https://doi.org/10.1007/s10208-015-9276-6 db/journals/focm/focm16.html#AdcockH16

Ben Adcock Rodrigo B. Platte A Mapped Polynomial Method for High-Accuracy Approximations on Arbitrary Grids. 2256-2281 2016 54 SIAM J. Numer. Anal. 4 https://doi.org/10.1137/15M1023853 db/journals/siamnum/siamnum54.html#AdcockP16

Ben Adcock Anders C. Hansen Bogdan Roman A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements. 732-736 2016 23 IEEE Signal Process. Lett. 5 https://doi.org/10.1109/LSP.2016.2550101 db/journals/spl/spl23.html#AdcockHR16

Alexander Daniel Jones Ben Adcock Anders C. Hansen On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems. 1020-1037 2016 62 IEEE Trans. Inf. Theory 2 https://doi.org/10.1109/TIT.2015.2508562 db/journals/tit/tit62.html#JonesAH16

Il Yong Chun Ben Adcock Thomas M. Talavage Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion. 354-368 2016 35 IEEE Trans. Medical Imaging 1 https://doi.org/10.1109/TMI.2015.2474383 https://www.wikidata.org/entity/Q40578153 db/journals/tmi/tmi35.html#ChunAT16

Il Yong Chun Chen Li Ben Adcock Sparsity and parallel acquisition: Optimal uniform and nonuniform recovery guarantees. 1-6 2016 ICME Workshops https://doi.org/10.1109/ICMEW.2016.7574710 https://doi.ieeecomputersociety.org/10.1109/ICMEW.2016.7574710 conf/icmcs/2016w db/conf/icmcs/icmew2016.html#ChunLA16 Il Yong Chun Ben Adcock Optimal sparse recovery for multi-sensor measurements. 270-274 2016 ITW https://doi.org/10.1109/ITW.2016.7606838 conf/itw/2016 db/conf/itw/itw2016.html#ChunA16

Chen Li Ben Adcock Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class. 2016 abs/1601.01988 CoRR http://arxiv.org/abs/1601.01988 db/journals/corr/corr1601.html#LiA16

Il Yong Chun Ben Adcock Compressed sensing and parallel acquisition. 2016 abs/1601.06214 CoRR http://arxiv.org/abs/1601.06214 db/journals/corr/corr1601.html#ChunA16

Il Yong Chun Ben Adcock Optimal Sparse Recovery for Multi-Sensor Measurements. 2016 abs/1603.06934 CoRR http://arxiv.org/abs/1603.06934 db/journals/corr/corr1603.html#ChunA16a

Il Yong Chun Chen Li Ben Adcock Sparsity and Parallel Acquisition: Optimal Uniform and Nonuniform Recovery Guarantees. 2016 abs/1603.08050 CoRR http://arxiv.org/abs/1603.08050 db/journals/corr/corr1603.html#ChunLA16

Il Yong Chun Ben Adcock Uniform Recovery from Subgaussian Multi-Sensor Measurements. 2016 abs/1610.05758 CoRR http://arxiv.org/abs/1610.05758 db/journals/corr/corr1610.html#ChunA16b

Alexander Daniel Jones Ben Adcock Anders C. Hansen Analyzing the structure of multidimensional compressed sensing problems through coherence. 2016 abs/1610.07497 CoRR http://arxiv.org/abs/1610.07497 db/journals/corr/corr1610.html#JonesAH16

Ben Adcock Daan Huybrechs Frames and numerical approximation. 2016 abs/1612.04464 CoRR http://arxiv.org/abs/1612.04464 db/journals/corr/corr1612.html#AdcockH16

Ben Adcock Anders C. Hansen Generalized sampling and the stable and accurate reconstruction of piecewise analytic functions from their Fourier coefficients. 2015 84 Math. Comput. 291 https://doi.org/10.1090/S0025-5718-2014-02860-3 db/journals/moc/moc84.html#AdcockH15 237-270

Ben Adcock Anders C. Hansen Gitta Kutyniok Jackie Ma Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements. 1196-1233 2015 47 SIAM J. Math. Anal. 2 https://doi.org/10.1137/140959365 db/journals/siamma/siamma47.html#AdcockHKM15

Ben Adcock Infinite-dimensional compressed sensing and function interpolation. 2015 abs/1509.06073 CoRR http://arxiv.org/abs/1509.06073 db/journals/corr/corr1509.html#Adcock15

Ben Adcock Daan Huybrechs Jesús Martín-Vaquero On the Numerical Stability of Fourier Extensions. 635-687 2014 14 Found. Comput. Math. 4 https://doi.org/10.1007/s10208-013-9158-8 https://www.wikidata.org/entity/Q117717434 db/journals/focm/focm14.html#AdcockHM14

Ben Adcock Daan Huybrechs On the resolution power of Fourier extensions for oscillatory functions. 312-336 2014 260 J. Comput. Appl. Math. https://doi.org/10.1016/j.cam.2013.09.069 https://www.wikidata.org/entity/Q117717439 db/journals/jcam/jcam260.html#AdcockH14

Ben Adcock Joseph Ruan Parameter selection and numerical approximation properties of Fourier extensions from fixed data. 453-471 2014 273 J. Comput. Phys. https://doi.org/10.1016/j.jcp.2014.05.036 db/journals/jcphy/jcphy273.html#AdcockR14

Ben Adcock Milana Gataric Anders C. Hansen On Stable Reconstructions from Nonuniform Fourier Measurements. 1690-1723 2014 7 SIAM J. Imaging Sci. 3 https://doi.org/10.1137/130943431 db/journals/siamis/siamis7.html#AdcockGH14

Ben Adcock Anders C. Hansen Alexei Shadrin A Stability Barrier for Reconstructions from Fourier Samples. 125-139 2014 52 SIAM J. Numer. Anal. 1 https://doi.org/10.1137/130908221 db/journals/siamnum/siamnum52.html#AdcockHS14

Ben Adcock Mark Richardson New Exponential Variable Transform Methods for Functions with Endpoint Singularities. 1887-1912 2014 52 SIAM J. Numer. Anal. 4 https://doi.org/10.1137/130920460 db/journals/siamnum/siamnum52.html#AdcockR14

Il Yong Chun Ben Adcock Thomas M. Talavage Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity promotion and mutual incoherence enhancement. 2424-2427 2014 EMBC https://doi.org/10.1109/EMBC.2014.6944111 https://www.wikidata.org/entity/Q39062703 conf/embc/2014 db/conf/embc/embc2014.html#ChunAT14 Il Yong Chun Ben Adcock Thomas M. Talavage Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem. 5141-5144 2014 EMBC https://doi.org/10.1109/EMBC.2014.6944782 https://www.wikidata.org/entity/Q41605490 conf/embc/2014 db/conf/embc/embc2014.html#ChunAT14a Kebina Manandhar Ben Adcock Xiaojun Cao Preserving the Anonymity in MobilityFirst networks. 1-6 2014 ICCCN https://doi.org/10.1109/ICCCN.2014.6911810 conf/icccn/2014 db/conf/icccn/icccn2014.html#ManandharAC14

Alexander Daniel Jones Ben Adcock Anders C. Hansen On Asymptotic Incoherence and its Implications for Compressed Sensing of Inverse Problems. 2014 abs/1402.5324 CoRR http://arxiv.org/abs/1402.5324 db/journals/corr/corr1402.html#JonesAH14

Ben Adcock Anders C. Hansen Bogdan Roman The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing. 2014 abs/1403.6540 CoRR http://arxiv.org/abs/1403.6540 db/journals/corr/corr1403.html#AdcockHR14

Bogdan Roman Anders C. Hansen Ben Adcock On asymptotic structure in compressed sensing. 2014 abs/1406.4178 CoRR http://arxiv.org/abs/1406.4178 db/journals/corr/corr1406.html#RomanHA14

Ben Adcock Anders C. Hansen Clarice Poon Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem. 3132-3167 2013 45 SIAM J. Math. Anal. 5 https://doi.org/10.1137/120895846 db/journals/siamma/siamma45.html#AdcockHP13

Ben Adcock Anders C. Hansen Clarice Poon Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem http://arxiv.org/abs/1301.2831 2013 CoRR abs/1301.2831 db/journals/corr/corr1301.html#abs-1301-2831

Ben Adcock Anders C. Hansen Clarice Poon Bogdan Roman Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing http://arxiv.org/abs/1302.0561 2013 CoRR abs/1302.0561 db/journals/corr/corr1302.html#abs-1302-0561

Ben Adcock Anders C. Hansen Bogdan Roman Gerd Teschke Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. 2013 CoRR http://arxiv.org/abs/1310.1141 abs/1310.1141 db/journals/corr/corr1310.html#AdcockHRT13

Ben Adcock Anders C. Hansen Clarice Poon On optimal wavelet reconstructions from Fourier samples: linearity and universality of the stable sampling rate http://arxiv.org/abs/1208.5959 2012 CoRR abs/1208.5959 db/journals/corr/corr1208.html#abs-1208-5959

Ben Adcock On the convergence of expansions in polyharmonic eigenfunctions. 1638-1674 2011 163 J. Approx. Theory 11 https://doi.org/10.1016/j.jat.2011.06.002 db/journals/jat/jat163.html#Adcock11

Ben Adcock Convergence acceleration of modified Fourier series in one or more dimensions. 225-261 2011 80 Math. Comput. 273 https://doi.org/10.1090/S0025-5718-2010-02393-2 db/journals/moc/moc80.html#Adcock11

Ben Adcock Multivariate modified Fourier series and application to boundary value problems. 511-552 2010 115 Numerische Mathematik 4 https://doi.org/10.1007/s00211-010-0287-6 db/journals/nm/nm115.html#Adcock10

Ben Adcock Anders C. Hansen A Generalized Sampling Theorem for Reconstructions in Arbitrary Bases http://arxiv.org/abs/1007.1852 2010 CoRR abs/1007.1852 db/journals/corr/corr1007.html#abs-1007-1852

Vegard Antun Anyi Bao Alexander Bastounis Claire Boyer Simone Brugiapaglia Paolo Campodonico Khiem Can Xiaojun Cao Juan M. Cardenas Il Yong Chun Matthew J. Colbrook Nick C. Dexter Jeffrey A. Fessler Milana Gataric Anne Gelb Nina Maria Gottschling Michael Griebel Avi Gupta Anders C. Hansen Astird Herremans Bernhard Hientzsch David Hong Nick Huang Daan Huybrechs John D. Jakeman Alexander Daniel Jones Matthew King-Roskamp Gitta Kutyniok Chen Li Jackie Ma Gregor Maier Kebina Manandhar J. Martín-VaqueroJesús Martín-Vaquero Akil Narayan 0001 Maksym Neyra-Nesterenko Rahul Parhi Cécile Piret Rodrigo B. Platte Clarice Poon Francesco Renna Mark Richardson Bogdan Roman Joseph Ruan Mihaela van der Schaar Sebastian Moraga ScheuermannSebastian Moraga Mohsen Seifi Alexei Shadrin Guohui Song Yi Sui Thomas M. Talavage Gerd Teschke Xuemeng Wang Qinghong Xu Yiming Xu 0009