Ben Adcock - RankMe

104 papers A* 3B 2Journal 94Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Christoffel Adaptive Sampling for Sparse Random Feature Expansions. CoRR Ben Adcock, Khiem Can, Xuemeng Wang
2025	J	`jnl`	Function recovery and optimal sampling in the presence of nonuniform evaluation costs. CoRR Ben Adcock
2025	J	`jnl`	How many measurements are enough? Bayesian recovery in inverse problems with general distributions. CoRR Ben Adcock, Nick Huang
2025	J	`jnl`	Hybrid least squares for learning functions from highly noisy data. CoRR Ben Adcock, Bernhard Hientzsch, Akil Narayan, Yiming Xu
2025	J	`jnl`	Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. Neural Networks Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2025	J	`jnl`	Optimal approximation of infinite-dimensional holomorphic functions II: Recovery from i.i.d. pointwise samples. J. Complex. Ben Adcock, Nick C. Dexter, Sebastian Moraga
2025	J	`jnl`	Optimal sampling for least-squares approximation. Found. Comput. Math. Ben Adcock
2025	J	`jnl`	Refinement-based Christoffel sampling for least squares approximation in non-orthogonal bases. CoRR Astird Herremans, Ben Adcock
2025	J	`jnl`	The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems. SIAM Rev. Nina Maria Gottschling, Vegard Antun, Anders C. Hansen, Ben Adcock
2025	J	`jnl`	Towards Sharp Minimax Risk Bounds for Operator Learning. CoRR Ben Adcock, Gregor Maier, Rahul Parhi
2024	A*	`conf`	A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. ICML Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2024	J	`jnl`	Learning Lipschitz Operators with respect to Gaussian Measures with Near-Optimal Sample Complexity. CoRR Ben Adcock, Michael Griebel, Gregor Maier
2024	J	`jnl`	Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2024	J	`jnl`	On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'. CoRR Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar, Ben Adcock, Anders C. Hansen
2024	A*	`conf`	Optimal deep learning of holomorphic operators between Banach spaces. NeurIPS Ben Adcock, Nick C. Dexter, Sebastian Moraga Scheuermann
2024	J	`jnl`	Optimal deep learning of holomorphic operators between Banach spaces. CoRR Ben Adcock, Nick C. Dexter, Sebastian Moraga
2024	J	`jnl`	Optimal sampling for least-squares approximation. CoRR Ben Adcock
2024	J	`jnl`	Stable and Accurate Least Squares Radial Basis Function Approximations on Bounded Domains. SIAM J. Numer. Anal. Ben Adcock, Daan Huybrechs, Cécile Piret
2023	J	`jnl`	A unified framework for learning with nonlinear model classes from arbitrary linear samples. CoRR Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2023	J	`jnl`	An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. SIAM J. Sci. Comput. Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2023	A*	`conf`	CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. NeurIPS Juan M. Cardenas, Ben Adcock, Nick C. Dexter
2023	J	`jnl`	CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. CoRR Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2023	J	`jnl`	Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples. CoRR Ben Adcock, Nick C. Dexter, Sebastian Moraga
2023	J	`jnl`	Optimal approximation of infinite-dimensional holomorphic functions. CoRR Ben Adcock, Nick C. Dexter, Sebastian Moraga
2023	J	`jnl`	Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods. CoRR Ben Adcock, Matthew J. Colbrook, Maksym Neyra-Nesterenko
2022	J	`jnl`	An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains. CoRR Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2022	J	`jnl`	CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. CoRR Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2022	J	`jnl`	Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing. Found. Comput. Math. Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2022	J	`jnl`	Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions? CoRR Ben Adcock, Simone Brugiapaglia
2022	J	`jnl`	Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2022	J	`jnl`	On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2022	J	`jnl`	Stable and accurate least squares radial basis function approximations on bounded domains. CoRR Ben Adcock, Daan Huybrechs, Cécile Piret
2022	J	`jnl`	Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models. CoRR Maksym Neyra-Nesterenko, Ben Adcock
2022	J	`jnl`	Towards optimal sampling for learning sparse approximation in high dimensions. CoRR Ben Adcock, Juan M. Cardenas, Nick C. Dexter, Sebastian Moraga
2021	—	`conf`	Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. MSML Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2021	J	`jnl`	Frame approximation with bounded coefficients. Adv. Comput. Math. Ben Adcock, Mohsen Seifi
2021	J	`jnl`	Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. SIAM J. Imaging Sci. Ben Adcock, Nick C. Dexter, Qinghong Xu
2021	J	`jnl`	Iterative and greedy algorithms for the sparsity in levels model in compressed sensing. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2021	—	`conf`	Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. AAAI Spring Symposium: MLPS Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2021	J	`jnl`	On the possibility of fast stable approximation of analytic functions from equispaced samples via polynomial frames. CoRR Ben Adcock, Alexei Shadrin
2021	J	`jnl`	The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees. IEEE Trans. Signal Process. Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2021	J	`jnl`	The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. SIAM J. Math. Data Sci. Ben Adcock, Nick C. Dexter
2020	J	`jnl`	Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. CoRR Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga
2020	J	`jnl`	Frame approximation with bounded coefficients. CoRR Ben Adcock, Mohsen Seifi
2020	J	`jnl`	Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging. CoRR Ben Adcock, Nick C. Dexter, Qinghong Xu
2020	J	`jnl`	Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. SIAM J. Math. Data Sci. Ben Adcock, Juan M. Cardenas
2020	J	`jnl`	The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2020	J	`jnl`	The gap between theory and practice in function approximation with deep neural networks. CoRR Ben Adcock, Nick C. Dexter
2020	J	`jnl`	The troublesome kernel: why deep learning for inverse problems is typically unstable. CoRR Nina Maria Gottschling, Vegard Antun, Ben Adcock, Anders C. Hansen
2019	J	`jnl`	Convolutional Analysis Operator Learning: Dependence on Training Data. CoRR Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler
2019	J	`jnl`	Convolutional Analysis Operator Learning: Dependence on Training Data. IEEE Signal Process. Lett. Il Yong Chun, David Hong, Ben Adcock, Jeffrey A. Fessler
2019	J	`jnl`	Correcting for unknown errors in sparse high-dimensional function approximation. Numerische Mathematik Ben Adcock, Anyi Bao, Simone Brugiapaglia
2019	J	`jnl`	Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing. CoRR Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
2019	J	`jnl`	Frames and Numerical Approximation. SIAM Rev. Ben Adcock, Daan Huybrechs
2019	J	`jnl`	Joint Sparse Recovery Based on Variances. SIAM J. Sci. Comput. Ben Adcock, Anne Gelb, Guohui Song, Yi Sui
2019	J	`jnl`	On instabilities of deep learning in image reconstruction - Does AI come at a cost? CoRR Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, Anders C. Hansen
2019	J	`jnl`	Optimal sampling strategies for multivariate function approximation on general domains. CoRR Ben Adcock, Juan M. Cardenas
2019	J	`jnl`	Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. CoRR Ben Adcock, Vegard Antun, Anders C. Hansen
2018	J	`jnl`	Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. SIAM/ASA J. Uncertain. Quantification Ben Adcock, Anyi Bao, John D. Jakeman, Akil Narayan
2018	J	`jnl`	Infinite-Dimensional Compressed Sensing and Function Interpolation. Found. Comput. Math. Ben Adcock
2018	J	`jnl`	On oracle-type local recovery guarantees in compressed sensing. CoRR Ben Adcock, Claire Boyer, Simone Brugiapaglia
2018	J	`jnl`	Robustness to Unknown Error in Sparse Regularization. IEEE Trans. Inf. Theory Simone Brugiapaglia, Ben Adcock
2018	J	`jnl`	Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit. CoRR Ben Adcock, Simone Brugiapaglia
2017	J	`jnl`	Compressed Sensing and Parallel Acquisition. IEEE Trans. Inf. Theory Il Yong Chun, Ben Adcock
2017	J	`jnl`	Resolution-Optimal Exponential and Double-Exponential Transform Methods for Functions with Endpoint Singularities. SIAM J. Sci. Comput. Ben Adcock, Jesús Martín-Vaquero, Mark Richardson
2017	J	`jnl`	Robustness to unknown error in sparse regularization. CoRR Simone Brugiapaglia, Ben Adcock
2016	J	`jnl`	A Mapped Polynomial Method for High-Accuracy Approximations on Arbitrary Grids. SIAM J. Numer. Anal. Ben Adcock, Rodrigo B. Platte
2016	J	`jnl`	A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements. IEEE Signal Process. Lett. Ben Adcock, Anders C. Hansen, Bogdan Roman
2016	J	`jnl`	Analyzing the structure of multidimensional compressed sensing problems through coherence. CoRR Alexander Daniel Jones, Ben Adcock, Anders C. Hansen
2016	J	`jnl`	Compressed sensing and parallel acquisition. CoRR Il Yong Chun, Ben Adcock
2016	J	`jnl`	Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class. CoRR Chen Li, Ben Adcock
2016	J	`jnl`	Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion. IEEE Trans. Medical Imaging Il Yong Chun, Ben Adcock, Thomas M. Talavage
2016	J	`jnl`	Frames and numerical approximation. CoRR Ben Adcock, Daan Huybrechs
2016	J	`jnl`	Generalized Sampling and Infinite-Dimensional Compressed Sensing. Found. Comput. Math. Ben Adcock, Anders C. Hansen
2016	J	`jnl`	On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems. IEEE Trans. Inf. Theory Alexander Daniel Jones, Ben Adcock, Anders C. Hansen
2016	J	`jnl`	Optimal Sparse Recovery for Multi-Sensor Measurements. CoRR Il Yong Chun, Ben Adcock
2016	B	`conf`	Optimal sparse recovery for multi-sensor measurements. ITW Il Yong Chun, Ben Adcock
2016	J	`jnl`	Sparsity and Parallel Acquisition: Optimal Uniform and Nonuniform Recovery Guarantees. CoRR Il Yong Chun, Chen Li, Ben Adcock
2016	—	`conf`	Sparsity and parallel acquisition: Optimal uniform and nonuniform recovery guarantees. ICME Workshops Il Yong Chun, Chen Li, Ben Adcock
2016	J	`jnl`	Uniform Recovery from Subgaussian Multi-Sensor Measurements. CoRR Il Yong Chun, Ben Adcock
2015	J	`jnl`	Generalized sampling and the stable and accurate reconstruction of piecewise analytic functions from their Fourier coefficients. Math. Comput. Ben Adcock, Anders C. Hansen
2015	J	`jnl`	Infinite-dimensional compressed sensing and function interpolation. CoRR Ben Adcock
2015	J	`jnl`	Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements. SIAM J. Math. Anal. Ben Adcock, Anders C. Hansen, Gitta Kutyniok, Jackie Ma
2014	J	`jnl`	A Stability Barrier for Reconstructions from Fourier Samples. SIAM J. Numer. Anal. Ben Adcock, Anders C. Hansen, Alexei Shadrin
2014	—	`conf`	Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity promotion and mutual incoherence enhancement. EMBC Il Yong Chun, Ben Adcock, Thomas M. Talavage
2014	J	`jnl`	New Exponential Variable Transform Methods for Functions with Endpoint Singularities. SIAM J. Numer. Anal. Ben Adcock, Mark Richardson
2014	—	`conf`	Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem. EMBC Il Yong Chun, Ben Adcock, Thomas M. Talavage
2014	J	`jnl`	On Asymptotic Incoherence and its Implications for Compressed Sensing of Inverse Problems. CoRR Alexander Daniel Jones, Ben Adcock, Anders C. Hansen
2014	J	`jnl`	On Stable Reconstructions from Nonuniform Fourier Measurements. SIAM J. Imaging Sci. Ben Adcock, Milana Gataric, Anders C. Hansen
2014	J	`jnl`	On asymptotic structure in compressed sensing. CoRR Bogdan Roman, Anders C. Hansen, Ben Adcock
2014	J	`jnl`	On the Numerical Stability of Fourier Extensions. Found. Comput. Math. Ben Adcock, Daan Huybrechs, Jesús Martín-Vaquero
2014	J	`jnl`	On the resolution power of Fourier extensions for oscillatory functions. J. Comput. Appl. Math. Ben Adcock, Daan Huybrechs
2014	J	`jnl`	Parameter selection and numerical approximation properties of Fourier extensions from fixed data. J. Comput. Phys. Ben Adcock, Joseph Ruan
2014	B	`conf`	Preserving the Anonymity in MobilityFirst networks. ICCCN Kebina Manandhar, Ben Adcock, Xiaojun Cao
2014	J	`jnl`	The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing. CoRR Ben Adcock, Anders C. Hansen, Bogdan Roman
2013	J	`jnl`	Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem. SIAM J. Math. Anal. Ben Adcock, Anders C. Hansen, Clarice Poon
2013	J	`jnl`	Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem CoRR Ben Adcock, Anders C. Hansen, Clarice Poon
2013	J	`jnl`	Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing CoRR Ben Adcock, Anders C. Hansen, Clarice Poon, Bogdan Roman
2013	J	`jnl`	Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. CoRR Ben Adcock, Anders C. Hansen, Bogdan Roman, Gerd Teschke
2012	J	`jnl`	On optimal wavelet reconstructions from Fourier samples: linearity and universality of the stable sampling rate CoRR Ben Adcock, Anders C. Hansen, Clarice Poon
2011	J	`jnl`	Convergence acceleration of modified Fourier series in one or more dimensions. Math. Comput. Ben Adcock
2011	J	`jnl`	On the convergence of expansions in polyharmonic eigenfunctions. J. Approx. Theory Ben Adcock
2010	J	`jnl`	A Generalized Sampling Theorem for Reconstructions in Arbitrary Bases CoRR Ben Adcock, Anders C. Hansen
2010	J	`jnl`	Multivariate modified Fourier series and application to boundary value problems. Numerische Mathematik Ben Adcock