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#p-krahmer

Conformal Prediction for Multi-Source Detection on a Network

AAAI 2026

#p-krahmer

Approximating Positive Homogeneous Functions With Scale Invariant Neural Networks

Journal of Approximation Theory 311.106177. Nov. 2025

#p-heckel #p-krahmer

On a Recovery Method With Approximation Guarantees for Noisy Unlimited Sampling

SampTA 2025

#p-krahmer

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

ICML 2025

#p-fornasier #p-krahmer

Stable Retrieval for Unlimited Sampling via Adaptive Local Representations

SSP 2025

#p-krahmer

Applied and Computational Harmonic Analysis 76.101746. Apr. 2025

#p-krahmer

The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoning

GAMM Mitteilungen 48.1. Mar. 2025

#p-fornasier #p-krahmer

Non-Intrusive Surrogate Modelling Using Sparse Random Features With Applications in Crashworthiness Analysis

International Journal for Uncertainty Quantification 15.4. Mar. 2025

#p-fornasier #p-krahmer

Solving Inverse Problems With Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent With Weight Decay

Preprint (Feb. 2025)

#p-krahmer

Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning

NeurIPS 2024

#p-krahmer #p-rauhut

Imaging With Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images

ECCV 2024

#p-krahmer #p-rauhut

With or Without Replacement? Improving Confidence in Fourier Imaging

CoSeRa 2024

#p-krahmer #p-rauhut

Noisy Recovery in Unlimited Sampling via Adaptive Modulo Representations

CoSeRa 2024

#p-krahmer

A One-Bit Quantization Approach for Low-Dose Poisson Phase Retrieval

CoSeRa 2024

#p-krahmer

COLT 2024

#p-krahmer

Uncertainty Quantification for Learned ISTA

MLSP 2023

#p-krahmer #p-rauhut

Uncertainty Quantification for Sparse Fourier Recovery

Preprint (Sep. 2023)

#p-krahmer #p-rauhut

In Vivo Myelin Water Quantification Using Diffusion--Relaxation Correlation MRI: A Comparison of 1D and 2D Methods

Applied Magnetic Resonance 54. Aug. 2023

#p-krahmer

Greedy-Type Sparse Recovery From Heavy-Tailed Measurements

SampTA 2023

#p-krahmer

Sampling Strategies for Compressive Imaging Under Statistical Noise

SampTA 2023

#p-krahmer #p-rauhut

Quantization of Bandlimited Functions Using Random Samples

SampTA 2023

#p-krahmer

Quantization of Bandlimited Graph Signals

SampTA 2023

#p-fornasier #p-krahmer

Digital Halftoning via Mixed-Order Weighted Σ∆ Modulation

SampTA 2023

#p-fornasier #p-krahmer

Enhanced Digital Halftoning via Weighted Sigma-Delta Modulation

SIAM Journal on Imaging Sciences 16.3. Jul. 2023

#p-fornasier #p-krahmer

Scalability in Ill-Posed Machine Learning Problems: Bridging Least Squares Methods With (Non-)Convex Algorithms

Dissertation TU München. Dec. 2022

#p-krahmer

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies

Frontiers in Public Health 9. Aug. 2021

#p-krahmer #p-theis

#p-krahmer

Conformal Prediction for Multi-Source Detection on a Network

Approximating Positive Homogeneous Functions With Scale Invariant Neural Networks

On a Recovery Method With Approximation Guarantees for Noisy Unlimited Sampling

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent

Stable Retrieval for Unlimited Sampling via Adaptive Local Representations

How Robust Is Randomized Blind Deconvolution via Nuclear Norm Minimization Against Adversarial Noise?

The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoning

Non-Intrusive Surrogate Modelling Using Sparse Random Features With Applications in Crashworthiness Analysis

Solving Inverse Problems With Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent With Weight Decay

Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning

Imaging With Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images

With or Without Replacement? Improving Confidence in Fourier Imaging

Noisy Recovery in Unlimited Sampling via Adaptive Modulo Representations

A One-Bit Quantization Approach for Low-Dose Poisson Phase Retrieval

Fast, Blind, and Accurate: Tuning-Free Sparse Regression With Global Linear Convergence

Uncertainty Quantification for Learned ISTA

Uncertainty Quantification for Sparse Fourier Recovery

In Vivo Myelin Water Quantification Using Diffusion--Relaxation Correlation MRI: A Comparison of 1D and 2D Methods

Greedy-Type Sparse Recovery From Heavy-Tailed Measurements

Sampling Strategies for Compressive Imaging Under Statistical Noise

Quantization of Bandlimited Functions Using Random Samples

Quantization of Bandlimited Graph Signals

Digital Halftoning via Mixed-Order Weighted Σ∆ Modulation

Enhanced Digital Halftoning via Weighted Sigma-Delta Modulation

Scalability in Ill-Posed Machine Learning Problems: Bridging Least Squares Methods With (Non-)Convex Algorithms

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies