Anders C. Hansen

Orcid: 0000-0003-2700-9446

According to our database1, Anders C. Hansen authored at least 32 papers between 2010 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
When can you trust feature selection? - I: A condition-based analysis of LASSO and generalised hardness of approximation.
CoRR, 2023

The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning.
CoRR, 2023

Implicit regularization in AI meets generalized hardness of approximation in optimization - Sharp results for diagonal linear networks.
CoRR, 2023

2022
Stratified Sampling Based Compressed Sensing for Structured Signals.
IEEE Trans. Signal Process., 2022

2021
The mathematics of adversarial attacks in AI - Why deep learning is unstable despite the existence of stable neural networks.
CoRR, 2021

Can stable and accurate neural networks be computed? - On the barriers of deep learning and Smale's 18th problem.
CoRR, 2021

2020
The troublesome kernel: why deep learning for inverse problems is typically unstable.
CoRR, 2020

2019
On the infinite-dimensional QR algorithm.
Numerische Mathematik, 2019

Non-uniform recovery guarantees for binary measurements and infinite-dimensional compressed sensing.
CoRR, 2019

On the stable sampling rate for binary measurements and wavelet reconstruction.
CoRR, 2019

Linear reconstructions and the analysis of the stable sampling rate.
CoRR, 2019

What do AI algorithms actually learn? - On false structures in deep learning.
CoRR, 2019

Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling.
CoRR, 2019

On instabilities of deep learning in image reconstruction - Does AI come at a cost?
CoRR, 2019

2017
On the Absence of Uniform Recovery in Many Real-World Applications of Compressed Sensing and the Restricted Isometry Property and Nullspace Property in Levels.
SIAM J. Imaging Sci., 2017

2016
On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems.
IEEE Trans. Inf. Theory, 2016

A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements.
IEEE Signal Process. Lett., 2016

Generalized Sampling and Infinite-Dimensional Compressed Sensing.
Found. Comput. Math., 2016

Analyzing the structure of multidimensional compressed sensing problems through coherence.
CoRR, 2016

2015
Linear Stable Sampling Rate: Optimality of 2D Wavelet Reconstructions from Fourier Measurements.
SIAM J. Math. Anal., 2015

Generalized sampling and the stable and accurate reconstruction of piecewise analytic functions from their Fourier coefficients.
Math. Comput., 2015

Can everything be computed? - On the Solvability Complexity Index and Towers of Algorithms.
CoRR, 2015

2014
A Stability Barrier for Reconstructions from Fourier Samples.
SIAM J. Numer. Anal., 2014

On Stable Reconstructions from Nonuniform Fourier Measurements.
SIAM J. Imaging Sci., 2014

On asymptotic structure in compressed sensing.
CoRR, 2014

On the absence of the RIP in real-world applications of compressed sensing and the RIP in levels.
CoRR, 2014

The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing.
CoRR, 2014

2013
Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem.
SIAM J. Math. Anal., 2013

Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing
CoRR, 2013

Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum.
CoRR, 2013

2012
On optimal wavelet reconstructions from Fourier samples: linearity and universality of the stable sampling rate
CoRR, 2012

2010
A Generalized Sampling Theorem for Reconstructions in Arbitrary Bases
CoRR, 2010


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