Tail bounds via generic chaining. - Sjoerd Dirksen (CoRR, 2013)

Uniform recovery of fusion frame structured sparse signals. - Ulas Ayaz; Sjoerd Dirksen; Holger Rauhut (CoRR, 2014)

Toward a Unified Theory of Sparse Dimensionality Reduction in Euclidean Space. - Jean Bourgain; Sjoerd Dirksen; Jelani Nelson (STOC, 2015)

On the gap between RIP-properties and sparse recovery conditions. - Sjoerd Dirksen; Guillaume Lecué; Holger Rauhut (CoRR, 2015)

Dimensionality Reduction with Subgaussian Matrices: A Unified Theory. - Sjoerd Dirksen (Found. Comput. Math., 2016)

One-bit compressed sensing with partial Gaussian circulant matrices. - Sjoerd Dirksen; Hans Christian Jung; Holger Rauhut (CoRR, 2017)

Robust one-bit compressed sensing with non-Gaussian measurements. - Sjoerd Dirksen; Shahar Mendelson (CoRR, 2018)

Robust one-bit compressed sensing with partial circulant matrices. - Sjoerd Dirksen; Shahar Mendelson (CoRR, 2018)

Fast Binary Embeddings with Gaussian Circulant Matrices: Improved Bounds. - Sjoerd Dirksen; Alexander Stollenwerk (Discret. Comput. Geom., 2018)

Gelfand numbers related to structured sparsity and Besov space embeddings with small mixed smoothness. - Sjoerd Dirksen; Tino Ullrich (J. Complex., 2018)

On the Gap Between Restricted Isometry Properties and Sparse Recovery Conditions. - Sjoerd Dirksen; Guillaume Lecué; Holger Rauhut (IEEE Trans. Inf. Theory, 2018)

Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs. - Simone Brugiapaglia; Sjoerd Dirksen; Hans Christian Jung; Holger Rauhut (CoRR, 2020)

Statistical post-processing of wind speed forecasts using convolutional neural networks. - Simon Veldkamp; Kirien Whan; Sjoerd Dirksen; Maurice Schmeits (CoRR, 2020)

Binarized Johnson-Lindenstrauss embeddings. - Sjoerd Dirksen; Alexander Stollenwerk (CoRR, 2020)

Covariance estimation under one-bit quantization. - Sjoerd Dirksen; Johannes Maly; Holger Rauhut (CoRR, 2021)

The Separation Capacity of Random Neural Networks. - Sjoerd Dirksen; Martin Genzel; Laurent Jacques; Alexander Stollenwerk (J. Mach. Learn. Res., 2022)

Sharp Estimates on Random Hyperplane Tessellations. - Sjoerd Dirksen; Shahar Mendelson; Alexander Stollenwerk (SIAM J. Math. Data Sci., 2022)

Memorization with neural nets: going beyond the worst case. - Sjoerd Dirksen; Patrick Finke; Martin Genzel (CoRR, 2023)

Fast Metric Embedding into the Hamming Cube. - Sjoerd Dirksen; Shahar Mendelson; Alexander Stollenwerk (SIAM J. Comput., 2024)

Plug-In Channel Estimation With Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems. - Tianyu Yang 002; Johannes Maly; Sjoerd Dirksen; Giuseppe Caire (IEEE Trans. Commun., 2024)

Tuning-Free One-Bit Covariance Estimation Using Data-Driven Dithering. - Sjoerd Dirksen; Johannes Maly (IEEE Trans. Inf. Theory, 2024)

Spectral method for low-dose Poisson and Bernoulli phase retrieval. - Sjoerd Dirksen; Felix Krahmer; Patricia Römer; Palina Salanevich (CoRR, 2025)

Subspace and DOA estimation under coarse quantization. - Sjoerd Dirksen; Weilin Li; Johannes Maly (CoRR, 2025)

Phasebook: A Survey of Selected Open Problems in Phase Retrieval. - Marc Allain; Selin Aslan; Wim Coene; Sjoerd Dirksen; Jonathan Dong; Julien Flamant; Mark A. Iwen; Felix Krahmer; Tristan van Leeuwen; Oleh Melnyk; Andreas Menzel; Allard P. Mosk; et al. (CoRR, 2025)

Near-optimal estimates for the ℓp-Lipschitz constants of deep random ReLU neural networks. - Sjoerd Dirksen; Patrick Finke; Paul Geuchen; Dominik Stöger; Felix Voigtländer (CoRR, 2025)