A Generalized Sampling Theorem for Reconstructions in Arbitrary Bases - Ben Adcock; Anders C. Hansen (CoRR, 2010)

Multivariate modified Fourier series and application to boundary value problems. - Ben Adcock (Numerische Mathematik, 2010)

On the convergence of expansions in polyharmonic eigenfunctions. - Ben Adcock (J. Approx. Theory, 2011)

Convergence acceleration of modified Fourier series in one or more dimensions. - Ben Adcock (Math. Comput., 2011)

On optimal wavelet reconstructions from Fourier samples: linearity and universality of the stable sampling rate - Ben Adcock; Anders C. Hansen; Clarice Poon (CoRR, 2012)

Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. - Ben Adcock; Anders C. Hansen; Bogdan Roman; Gerd Teschke (CoRR, 2013)

Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing - Ben Adcock; Anders C. Hansen; Clarice Poon; Bogdan Roman (CoRR, 2013)

Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem. - Ben Adcock; Anders C. Hansen; Clarice Poon (SIAM J. Math. Anal., 2013)

Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity promotion and mutual incoherence enhancement. - Il Yong Chun; Ben Adcock; Thomas M. Talavage (EMBC, 2014)

Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem. - Il Yong Chun; Ben Adcock; Thomas M. Talavage (EMBC, 2014)

Preserving the Anonymity in MobilityFirst networks. - Kebina Manandhar; Ben Adcock; Xiaojun Cao (ICCCN, 2014)

The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing. - Ben Adcock; Anders C. Hansen; Bogdan Roman (CoRR, 2014)

On asymptotic structure in compressed sensing. - Bogdan Roman; Anders C. Hansen; Ben Adcock (CoRR, 2014)

On the Numerical Stability of Fourier Extensions. - Ben Adcock; Daan Huybrechs; Jesús Martín-Vaquero (Found. Comput. Math., 2014)

On the resolution power of Fourier extensions for oscillatory functions. - Ben Adcock; Daan Huybrechs (J. Comput. Appl. Math., 2014)

Parameter selection and numerical approximation properties of Fourier extensions from fixed data. - Ben Adcock; Joseph Ruan (J. Comput. Phys., 2014)

On Stable Reconstructions from Nonuniform Fourier Measurements. - Ben Adcock; Milana Gataric; Anders C. Hansen (SIAM J. Imaging Sci., 2014)

A Stability Barrier for Reconstructions from Fourier Samples. - Ben Adcock; Anders C. Hansen; Alexei Shadrin (SIAM J. Numer. Anal., 2014)

New Exponential Variable Transform Methods for Functions with Endpoint Singularities. - Ben Adcock; Mark Richardson (SIAM J. Numer. Anal., 2014)

Generalized sampling and the stable and accurate reconstruction of piecewise analytic functions from their Fourier coefficients. - Ben Adcock; Anders C. Hansen (Math. Comput., 2015)

Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements. - Ben Adcock; Anders C. Hansen; Gitta Kutyniok; Jackie Ma (SIAM J. Math. Anal., 2015)

Sparsity and parallel acquisition: Optimal uniform and nonuniform recovery guarantees. - Il Yong Chun; Chen Li; Ben Adcock (ICME Workshops, 2016)

Optimal sparse recovery for multi-sensor measurements. - Il Yong Chun; Ben Adcock (ITW, 2016)

Uniform Recovery from Subgaussian Multi-Sensor Measurements. - Il Yong Chun; Ben Adcock (CoRR, 2016)

Analyzing the structure of multidimensional compressed sensing problems through coherence. - Alexander Daniel Jones; Ben Adcock; Anders C. Hansen (CoRR, 2016)

Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class. - Chen Li; Ben Adcock (CoRR, 2016)

Generalized Sampling and Infinite-Dimensional Compressed Sensing. - Ben Adcock; Anders C. Hansen (Found. Comput. Math., 2016)

A Mapped Polynomial Method for High-Accuracy Approximations on Arbitrary Grids. - Ben Adcock; Rodrigo B. Platte (SIAM J. Numer. Anal., 2016)

A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements. - Ben Adcock; Anders C. Hansen; Bogdan Roman (IEEE Signal Process. Lett., 2016)

On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems. - Alexander Daniel Jones; Ben Adcock; Anders C. Hansen (IEEE Trans. Inf. Theory, 2016)

Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion. - Il Yong Chun; Ben Adcock; Thomas M. Talavage (IEEE Trans. Medical Imaging, 2016)

Resolution-Optimal Exponential and Double-Exponential Transform Methods for Functions with Endpoint Singularities. - Ben Adcock; Jesús Martín-Vaquero; Mark Richardson (SIAM J. Sci. Comput., 2017)

Compressed Sensing and Parallel Acquisition. - Il Yong Chun; Ben Adcock (IEEE Trans. Inf. Theory, 2017)

On oracle-type local recovery guarantees in compressed sensing. - Ben Adcock; Claire Boyer; Simone Brugiapaglia (CoRR, 2018)

Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. - Ben Adcock; Simone Brugiapaglia (CoRR, 2018)

Infinite-Dimensional Compressed Sensing and Function Interpolation. - Ben Adcock (Found. Comput. Math., 2018)

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. - Ben Adcock; Anyi Bao; John D. Jakeman; Akil Narayan 001 (SIAM/ASA J. Uncertain. Quantification, 2018)

Robustness to Unknown Error in Sparse Regularization. - Simone Brugiapaglia; Ben Adcock (IEEE Trans. Inf. Theory, 2018)

On instabilities of deep learning in image reconstruction - Does AI come at a cost? - Vegard Antun; Francesco Renna; Clarice Poon; Ben Adcock; Anders C. Hansen (CoRR, 2019)

Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. - Ben Adcock; Vegard Antun; Anders C. Hansen (CoRR, 2019)

Optimal sampling strategies for multivariate function approximation on general domains. - Ben Adcock; Juan M. Cardenas (CoRR, 2019)

Correcting for unknown errors in sparse high-dimensional function approximation. - Ben Adcock; Anyi Bao; Simone Brugiapaglia (Numerische Mathematik, 2019)

Frames and Numerical Approximation. - Ben Adcock; Daan Huybrechs (SIAM Rev., 2019)

Joint Sparse Recovery Based on Variances. - Ben Adcock; Anne Gelb; Guohui Song; Yi Sui (SIAM J. Sci. Comput., 2019)

Convolutional Analysis Operator Learning: Dependence on Training Data. - Il Yong Chun; David Hong; Ben Adcock; Jeffrey A. Fessler (IEEE Signal Process. Lett., 2019)

The troublesome kernel: why deep learning for inverse problems is typically unstable. - Nina Maria Gottschling; Vegard Antun; Ben Adcock; Anders C. Hansen (CoRR, 2020)

Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. - Ben Adcock; Juan M. Cardenas (SIAM J. Math. Data Sci., 2020)

Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (AAAI Spring Symposium: MLPS, 2021)

Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (MSML, 2021)

Frame approximation with bounded coefficients. - Ben Adcock; Mohsen Seifi (Adv. Comput. Math., 2021)

On the possibility of fast stable approximation of analytic functions from equispaced samples via polynomial frames. - Ben Adcock; Alexei Shadrin (CoRR, 2021)

Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (CoRR, 2021)

Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. - Ben Adcock; Nick C. Dexter; Qinghong Xu (SIAM J. Imaging Sci., 2021)

The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. - Ben Adcock; Nick C. Dexter (SIAM J. Math. Data Sci., 2021)

The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (IEEE Trans. Signal Process., 2021)

Towards optimal sampling for learning sparse approximation in high dimensions. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter; Sebastian Moraga (CoRR, 2022)

Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models. - Maksym Neyra-Nesterenko; Ben Adcock (CoRR, 2022)

On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2022)

Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? - Ben Adcock; Simone Brugiapaglia (CoRR, 2022)

CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (CoRR, 2022)

Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. - Ben Adcock; Simone Brugiapaglia; Matthew King-Roskamp (Found. Comput. Math., 2022)

CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. - Juan M. Cardenas; Ben Adcock; Nick C. Dexter (NeurIPS, 2023)

Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods. - Ben Adcock; Matthew J. Colbrook; Maksym Neyra-Nesterenko (CoRR, 2023)

Optimal approximation of infinite-dimensional holomorphic functions. - Ben Adcock; Nick C. Dexter; Sebastian Moraga (CoRR, 2023)

An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (SIAM J. Sci. Comput., 2023)

A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (ICML, 2024)

Optimal deep learning of holomorphic operators between Banach spaces. - Ben Adcock; Nick C. Dexter; Sebastian Moraga Scheuermann (NeurIPS, 2024)

Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2024)

On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'. - Alexander Bastounis; Paolo Campodonico; Mihaela van der Schaar; Ben Adcock; Anders C. Hansen (CoRR, 2024)

Optimal sampling for least-squares approximation. - Ben Adcock (CoRR, 2024)

Learning Lipschitz Operators with respect to Gaussian Measures with Near-Optimal Sample Complexity. - Ben Adcock; Michael Griebel; Gregor Maier (CoRR, 2024)

Stable and Accurate Least Squares Radial Basis Function Approximations on Bounded Domains. - Ben Adcock; Daan Huybrechs; Cécile Piret (SIAM J. Numer. Anal., 2024)

Optimal approximation of infinite-dimensional holomorphic functions II: Recovery from i.i.d. pointwise samples. - Ben Adcock; Nick C. Dexter; Sebastian Moraga (J. Complex., 2025)

Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (Neural Networks, 2025)

The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems. - Nina Maria Gottschling; Vegard Antun; Anders C. Hansen; Ben Adcock (SIAM Rev., 2025)

Function recovery and optimal sampling in the presence of nonuniform evaluation costs. - Ben Adcock (CoRR, 2025)

How many measurements are enough? Bayesian recovery in inverse problems with general distributions. - Ben Adcock; Nick Huang (CoRR, 2025)

Hybrid least squares for learning functions from highly noisy data. - Ben Adcock; Bernhard Hientzsch; Akil Narayan 001; Yiming Xu (CoRR, 2025)