Sampling numbers and function spaces. - Jan Vybíral (J. Complex., 2007)

Linear information versus function evaluations for L2-approximation. - Aicke Hinrichs; Erich Novak; Jan Vybíral (J. Approx. Theory, 2008)

Widths of embeddings in function spaces. - Jan Vybíral (J. Complex., 2008)

Corrigendum to the paper: "On approximation numbers of Sobolev embeddings of weighted function spaces" [J. Approx. Theory 136 (2005) 91-107]. - Leszek Skrzypczak; Jan Vybíral (J. Approx. Theory, 2009)

Compressed learning of high-dimensional sparse functions. - Karin Schnass; Jan Vybíral (ICASSP, 2011)

On positive positive-definite functions and Bochner's Theorem. - Aicke Hinrichs; Jan Vybíral (J. Complex., 2011)

Particle Systems and Kinetic Equations Modeling Interacting Agents in High Dimension. - Massimo Fornasier; Jan Haskovec; Jan Vybíral (Multiscale Model. Simul., 2011)

Johnson-Lindenstrauss lemma for circulant matrices. - Aicke Hinrichs; Jan Vybíral (Random Struct. Algorithms, 2011)

Learning Functions of Few Arbitrary Linear Parameters in High Dimensions. - Massimo Fornasier; Karin Schnass; Jan Vybíral (Found. Comput. Math., 2012)

Weak and quasi-polynomial tractability of approximation of infinitely differentiable functions. - Jan Vybíral (J. Complex., 2014)

On some aspects of approximation of ridge functions. - Anton Kolleck; Jan Vybíral (J. Approx. Theory, 2015)

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. - Tim O. F. Conrad; Martin Genzel; Nada Cvetkovic; Niklas Wulkow; Alexander B. Leichtle; Jan Vybíral; Gitta Kutyniok; Christof Schütte (BMC Bioinform., 2017)

Non-Asymptotic Analysis of ℓ1-Norm Support Vector Machines. - Anton Kolleck; Jan Vybíral (IEEE Trans. Inf. Theory, 2017)

Identification of Shallow Neural Networks by Fewest Samples. - Massimo Fornasier; Jan Vybíral; Ingrid Daubechies (CoRR, 2018)

An upper bound on the minimal dispersion. - Mario Ullrich; Jan Vybíral (J. Complex., 2018)

A variant of Schur's product theorem and its applications. - Jan Vybíral (CoRR, 2019)

The minimal k-dispersion of point sets in high dimensions. - Aicke Hinrichs; Joscha Prochno; Mario Ullrich; Jan Vybíral (J. Complex., 2019)

On the volume of unit balls of finite-dimensional Lorentz spaces. - Anna Dolezalová; Jan Vybíral (J. Approx. Theory, 2020)

Lower bounds for integration and recovery in L2. - Aicke Hinrichs; David Krieg 001; Erich Novak; Jan Vybíral (CoRR, 2021)

Lower bounds for the error of quadrature formulas for Hilbert spaces. - Aicke Hinrichs; David Krieg 001; Erich Novak; Jan Vybíral (J. Complex., 2021)

Deterministic Constructions of High-Dimensional Sets with Small Dispersion. - Mario Ullrich; Jan Vybíral (Algorithmica, 2022)

Lower bounds for integration and recovery in L2. - Aicke Hinrichs; David Krieg 001; Erich Novak; Jan Vybíral (J. Complex., 2022)

New lower bounds for the integration of periodic functions. - David Krieg 001; Jan Vybíral (CoRR, 2023)

A multivariate Riesz basis of ReLU neural networks. - Cornelia Schneider; Jan Vybíral (CoRR, 2023)

Algorithms and Complexity for Continuous Problems (Dagstuhl Seminar 23351). - Dmitriy Bilyk; Michael Gnewuch; Jan Vybíral; Larisa Yaroslavtseva; Kumar Harsha (Dagstuhl Reports, 2023)

Entropy numbers of finite-dimensional Lorentz space embeddings. - Joscha Prochno; Mathias Sonnleitner; Jan Vybíral (CoRR, 2024)

A tight lower bound on the minimal dispersion. - Matej Trödler; Jan Volec; Jan Vybíral (Eur. J. Comb., 2024)

Robust network formation with biological applications. - Jan Haskovec; Jan Vybíral (Networks Heterog. Media, 2024)

Stefan Heinrich is the Winner of the 2024 Best Paper Award of the Journal of Complexity. - Erich Novak; Mario Ullrich; Jan Vybíral (J. Complex., 2025)

Lower bounds on the minimal dispersion of point sets via cover-free families. - Matej Trödler; Jan Volec; Jan Vybíral (J. Complex., 2025)

Nonlocal techniques for the analysis of deep ReLU neural network approximations. - Cornelia Schneider; Mario Ullrich; Jan Vybíral (CoRR, 2025)