Author

Mathias Staudigl

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Preprint Oct 2026

Computing Data-Aware Diagonal Frame Decompositions

We present a method for computing a specific type of matrix decomposition that combines a structured framework with data-driven adaptation. This framework allows for an efficient and stable representation of signals or images using frames by identifying sets of vectors that naturally capture the essential features of t...

Andrea Ebner, D. Lorenz, Mathias Staudigl · 0 citations

Preprint Oct 2026

Multilevel Monte Carlo for Zeroth-Order Stochastic Bilevel Optimization with Inexact Lower Level Solves

Variational regularization techniques are dominant in the field of mathematical imaging. The expressiveness of these methods heavily depends on the adopted regularization formulation which usually involves a number of parameters which have to be fine-tuned by the user. To circumvent the heavy computational costs associ...

N. K. Chada, Mohammad Sadegh Salehi, Mathias Staudigl et al. · 1 citation

#machine learning Preprint Sep 2026

Stochastic Nonconvex Bilevel Optimization: Improved Rates Without Rare-Visit Assumption

This work shows that a simple denominator-only regularization of the DBGD multiplier eliminates the need for such an assumption while preserving fast convergence rates, and improves upon the best assumption-free complexities.

Daniel Cortild, Mathias Staudigl, J. Peypouquet et al. · 0 citations