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2020 – today
- 2024
[c9]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. ICML 2024- [i49]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR abs/2404.03761 (2024) - [i48]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal deep learning of holomorphic operators between Banach spaces. CoRR abs/2406.13928 (2024) - [i47]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'. CoRR abs/2408.02357 (2024) - [i46]Ben Adcock:
Optimal sampling for least-squares approximation. CoRR abs/2409.02342 (2024) - 2023
- [j33]Ben Adcock, Juan M. Cardenas
, Nick C. Dexter:
An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. SIAM J. Sci. Comput. 45(1): 200- (2023) - [c8]Juan M. Cardenas, Ben Adcock, Nick C. Dexter:
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. NeurIPS 2023 - [i45]Ben Adcock, Matthew J. Colbrook, Maksym Neyra-Nesterenko:
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods. CoRR abs/2301.02268 (2023) - [i44]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal approximation of infinite-dimensional holomorphic functions. CoRR abs/2305.18642 (2023) - [i43]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. CoRR abs/2306.00945 (2023) - [i42]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples. CoRR abs/2310.16940 (2023) - [i41]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
A unified framework for learning with nonlinear model classes from arbitrary linear samples. CoRR abs/2311.14886 (2023) - 2022
- [j32]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. Found. Comput. Math. 22(1): 99-159 (2022) - [i40]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains. CoRR abs/2202.00144 (2022) - [i39]Ben Adcock, Juan M. Cardenas, Nick C. Dexter, Sebastian Moraga:
Towards optimal sampling for learning sparse approximation in high dimensions. CoRR abs/2202.02360 (2022) - [i38]Maksym Neyra-Nesterenko, Ben Adcock:
Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models. CoRR abs/2203.00804 (2022) - [i37]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. CoRR abs/2203.13908 (2022) - [i36]Ben Adcock, Simone Brugiapaglia:
Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? CoRR abs/2208.09045 (2022) - [i35]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. CoRR abs/2208.12190 (2022) - [i34]Ben Adcock, Daan Huybrechs, Cécile Piret:
Stable and accurate least squares radial basis function approximations on bounded domains. CoRR abs/2211.12598 (2022) - [i33]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR abs/2211.12633 (2022) - 2021
- [j31]Ben Adcock, Mohsen Seifi:
Frame approximation with bounded coefficients. Adv. Comput. Math. 47(1): 4 (2021) - [j30]Ben Adcock, Nick C. Dexter, Qinghong Xu:
Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. SIAM J. Imaging Sci. 14(3): 1149-1183 (2021) - [j29]Ben Adcock, Nick C. Dexter:
The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. SIAM J. Math. Data Sci. 3(2): 624-655 (2021) - [j28]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. IEEE Trans. Signal Process. 69: 3160-3175 (2021) - [c7]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. AAAI Spring Symposium: MLPS 2021 - [c6]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. MSML 2021: 1-36 - [i32]Ben Adcock, Alexei Shadrin:
On the possibility of fast stable approximation of analytic functions from equispaced samples via polynomial frames. CoRR abs/2110.03755 (2021) - [i31]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. CoRR abs/2110.15420 (2021) - 2020
- [j27]Ben Adcock, Juan M. Cardenas:
Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. SIAM J. Math. Data Sci. 2(3): 607-630 (2020) - [i30]Ben Adcock, Mohsen Seifi:
Frame approximation with bounded coefficients. CoRR abs/2001.00983 (2020) - [i29]Nina M. Gottschling, Vegard Antun, Ben Adcock, Anders C. Hansen:
The troublesome kernel: why deep learning for inverse problems is typically unstable. CoRR abs/2001.01258 (2020) - [i28]Ben Adcock, Nick C. Dexter:
The gap between theory and practice in function approximation with deep neural networks. CoRR abs/2001.07523 (2020) - [i27]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. CoRR abs/2006.13389 (2020) - [i26]Ben Adcock, Nick C. Dexter, Qinghong Xu:
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging. CoRR abs/2009.08555 (2020) - [i25]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. CoRR abs/2012.06081 (2020)
2010 – 2019
- 2019
- [j26]Ben Adcock, Anyi Bao, Simone Brugiapaglia:
Correcting for unknown errors in sparse high-dimensional function approximation. Numerische Mathematik 142(3): 667-711 (2019) - [j25]Ben Adcock, Daan Huybrechs:
Frames and Numerical Approximation. SIAM Rev. 61(3): 443-473 (2019) - [j24]Ben Adcock, Anne Gelb, Guohui Song, Yi Sui:
Joint Sparse Recovery Based on Variances. SIAM J. Sci. Comput. 41(1): A246-A268 (2019) - [j23]Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler:
Convolutional Analysis Operator Learning: Dependence on Training Data. IEEE Signal Process. Lett. 26(8): 1137-1141 (2019) - [i24]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? CoRR abs/1902.05300 (2019) - [i23]Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler:
Convolutional Analysis Operator Learning: Dependence on Training Data. CoRR abs/1902.08267 (2019) - [i22]Ben Adcock, Vegard Antun, Anders C. Hansen:
Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. CoRR abs/1905.00126 (2019) - [i21]Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp:
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. CoRR abs/1905.10028 (2019) - [i20]Ben Adcock, Juan M. Cardenas:
Optimal sampling strategies for multivariate function approximation on general domains. CoRR abs/1908.01249 (2019) - 2018
- [j22]Ben Adcock:
Infinite-Dimensional Compressed Sensing and Function Interpolation. Found. Comput. Math. 18(3): 661-701 (2018) - [j21]Ben Adcock, Anyi Bao, John D. Jakeman, Akil Narayan:
Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. SIAM/ASA J. Uncertain. Quantification 6(4): 1424-1453 (2018) - [j20]Simone Brugiapaglia, Ben Adcock:
Robustness to Unknown Error in Sparse Regularization. IEEE Trans. Inf. Theory 64(10): 6638-6661 (2018) - [i19]Ben Adcock, Claire Boyer, Simone Brugiapaglia:
On oracle-type local recovery guarantees in compressed sensing. CoRR abs/1806.03789 (2018) - [i18]Ben Adcock, Simone Brugiapaglia:
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. CoRR abs/1810.11115 (2018) - 2017
- [j19]Ben Adcock, Jesús Martín-Vaquero, Mark Richardson:
Resolution-Optimal Exponential and Double-Exponential Transform Methods for Functions with Endpoint Singularities. SIAM J. Sci. Comput. 39(1) (2017) - [j18]Il Yong Chun, Ben Adcock:
Compressed Sensing and Parallel Acquisition. IEEE Trans. Inf. Theory 63(8): 4860-4882 (2017) - [i17]Simone Brugiapaglia, Ben Adcock:
Robustness to unknown error in sparse regularization. CoRR abs/1705.10299 (2017) - 2016
- [j17]Ben Adcock, Anders C. Hansen:
Generalized Sampling and Infinite-Dimensional Compressed Sensing. Found. Comput. Math. 16(5): 1263-1323 (2016) - [j16]Ben Adcock, Rodrigo B. Platte:
A Mapped Polynomial Method for High-Accuracy Approximations on Arbitrary Grids. SIAM J. Numer. Anal. 54(4): 2256-2281 (2016) - [j15]Ben Adcock, Anders C. Hansen, Bogdan Roman:
A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements. IEEE Signal Process. Lett. 23(5): 732-736 (2016) - [j14]Alexander Daniel Jones, Ben Adcock, Anders C. Hansen:
On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems. IEEE Trans. Inf. Theory 62(2): 1020-1037 (2016) - [j13]Il Yong Chun, Ben Adcock, Thomas M. Talavage:
Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion. IEEE Trans. Medical Imaging 35(1): 354-368 (2016) - [c5]Il Yong Chun, Chen Li, Ben Adcock:
Sparsity and parallel acquisition: Optimal uniform and nonuniform recovery guarantees. ICME Workshops 2016: 1-6 - [c4]Il Yong Chun, Ben Adcock:
Optimal sparse recovery for multi-sensor measurements. ITW 2016: 270-274 - [i16]Chen Li, Ben Adcock:
Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class. CoRR abs/1601.01988 (2016) - [i15]Il Yong Chun, Ben Adcock:
Compressed sensing and parallel acquisition. CoRR abs/1601.06214 (2016) - [i14]Il Yong Chun, Ben Adcock:
Optimal Sparse Recovery for Multi-Sensor Measurements. CoRR abs/1603.06934 (2016) - [i13]Il Yong Chun, Chen Li, Ben Adcock:
Sparsity and Parallel Acquisition: Optimal Uniform and Nonuniform Recovery Guarantees. CoRR abs/1603.08050 (2016) - [i12]Il Yong Chun, Ben Adcock:
Uniform Recovery from Subgaussian Multi-Sensor Measurements. CoRR abs/1610.05758 (2016) - [i11]Alexander Daniel Jones, Ben Adcock, Anders C. Hansen:
Analyzing the structure of multidimensional compressed sensing problems through coherence. CoRR abs/1610.07497 (2016) - [i10]Ben Adcock, Daan Huybrechs:
Frames and numerical approximation. CoRR abs/1612.04464 (2016) - 2015
- [j12]Ben Adcock, Anders C. Hansen:
Generalized sampling and the stable and accurate reconstruction of piecewise analytic functions from their Fourier coefficients. Math. Comput. 84(291): 237-270 (2015) - [j11]Ben Adcock, Anders C. Hansen, Gitta Kutyniok, Jackie Ma:
Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements. SIAM J. Math. Anal. 47(2): 1196-1233 (2015) - [i9]Ben Adcock:
Infinite-dimensional compressed sensing and function interpolation. CoRR abs/1509.06073 (2015) - 2014
- [j10]Ben Adcock, Daan Huybrechs, Jesús Martín-Vaquero:
On the Numerical Stability of Fourier Extensions. Found. Comput. Math. 14(4): 635-687 (2014) - [j9]Ben Adcock, Daan Huybrechs:
On the resolution power of Fourier extensions for oscillatory functions. J. Comput. Appl. Math. 260: 312-336 (2014) - [j8]Ben Adcock, Joseph Ruan:
Parameter selection and numerical approximation properties of Fourier extensions from fixed data. J. Comput. Phys. 273: 453-471 (2014) - [j7]Ben Adcock, Milana Gataric, Anders C. Hansen:
On Stable Reconstructions from Nonuniform Fourier Measurements. SIAM J. Imaging Sci. 7(3): 1690-1723 (2014) - [j6]Ben Adcock, Anders C. Hansen, Alexei Shadrin:
A Stability Barrier for Reconstructions from Fourier Samples. SIAM J. Numer. Anal. 52(1): 125-139 (2014) - [j5]Ben Adcock, Mark Richardson:
New Exponential Variable Transform Methods for Functions with Endpoint Singularities. SIAM J. Numer. Anal. 52(4): 1887-1912 (2014) - [c3]Il Yong Chun, Ben Adcock, Thomas M. Talavage:
Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity promotion and mutual incoherence enhancement. EMBC 2014: 2424-2427 - [c2]Il Yong Chun, Ben Adcock, Thomas M. Talavage:
Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem. EMBC 2014: 5141-5144 - [c1]Kebina Manandhar, Ben Adcock, Xiaojun Cao:
Preserving the Anonymity in MobilityFirst networks. ICCCN 2014: 1-6 - [i8]Alexander Daniel Jones, Ben Adcock, Anders C. Hansen:
On Asymptotic Incoherence and its Implications for Compressed Sensing of Inverse Problems. CoRR abs/1402.5324 (2014) - [i7]Ben Adcock, Anders C. Hansen, Bogdan Roman:
The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing. CoRR abs/1403.6540 (2014) - [i6]Bogdan Roman, Anders C. Hansen, Ben Adcock:
On asymptotic structure in compressed sensing. CoRR abs/1406.4178 (2014) - 2013
- [j4]Ben Adcock, Anders C. Hansen, Clarice Poon:
Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem. SIAM J. Math. Anal. 45(5): 3132-3167 (2013) - [i5]Ben Adcock, Anders C. Hansen, Clarice Poon:
Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem. CoRR abs/1301.2831 (2013) - [i4]Ben Adcock, Anders C. Hansen, Clarice Poon, Bogdan Roman:
Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing. CoRR abs/1302.0561 (2013) - [i3]Ben Adcock, Anders C. Hansen, Bogdan Roman, Gerd Teschke:
Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. CoRR abs/1310.1141 (2013) - 2012
- [i2]Ben Adcock, Anders C. Hansen, Clarice Poon:
On optimal wavelet reconstructions from Fourier samples: linearity and universality of the stable sampling rate. CoRR abs/1208.5959 (2012) - 2011
- [j3]Ben Adcock:
On the convergence of expansions in polyharmonic eigenfunctions. J. Approx. Theory 163(11): 1638-1674 (2011) - [j2]Ben Adcock:
Convergence acceleration of modified Fourier series in one or more dimensions. Math. Comput. 80(273): 225-261 (2011) - 2010
- [j1]Ben Adcock:
Multivariate modified Fourier series and application to boundary value problems. Numerische Mathematik 115(4): 511-552 (2010) - [i1]Ben Adcock, Anders C. Hansen:
A Generalized Sampling Theorem for Reconstructions in Arbitrary Bases. CoRR abs/1007.1852 (2010)
Coauthor Index
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last updated on 2024-10-22 20:11 CEST by the dblp team
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