publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2026

  1. Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
    Berk Hayta, Hannah Laus, Simon Mittermaier, and 1 more author
    arXiv preprint arXiv:2605.22746, 2026
  2. KV Cache Compression Through the Lens of Transform Coding
    Hannah Laus, Claudio Mayrink Verdun, Hao Wang, and 2 more authors
    arXiv preprint arXiv:2608.14191, 2026

2025

  1. Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
    Hannah Laus, Suzanna Parkinson, Vasileios Charisopoulos, and 2 more authors
    arXiv preprint arXiv:2502.15522, 2025

2024

  1. Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
    Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus, and 2 more authors
    Advances in Neural Information Processing Systems, 2024
  2. Imaging with Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images
    Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus, and 4 more authors
    In European Conference on Computer Vision, 2024

2023

  1. Uncertainty quantification for learned ista
    Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus, and 2 more authors
    In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023