Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares. - Christian Kümmerle; Claudio Mayrink Verdun (CoRR, 2018)

Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method. - Christian Kümmerle; Claudio Mayrink Verdun (CoRR, 2020)

Iteratively Reweighted Least Squares for 𝓁1-minimization with Global Linear Convergence Rate. - Christian Kümmerle; Claudio Mayrink Verdun; Dominik Stöger (CoRR, 2020)

A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples. - Christian Kümmerle; Claudio Mayrink Verdun (ICML, 2021)

Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. - Christian Kümmerle; Claudio Mayrink Verdun; Dominik Stöger (NeurIPS, 2021)

Non-negative Least Squares via Overparametrization. - Hung-Hsu Chou; Johannes Maly; Claudio Mayrink Verdun (CoRR, 2022)

Uncertainty quantification for sparse Fourier recovery. - Frederik Hoppe; Felix Krahmer; Claudio Mayrink Verdun; Marion I. Menzel; Holger Rauhut (CoRR, 2022)

High-Dimensional Confidence Regions in Sparse MRI. - Frederik Hoppe; Felix Krahmer; Claudio Mayrink Verdun; Marion I. Menzel; Holger Rauhut (ICASSP, 2023)

Uncertainty Quantification For Learned ISTA. - Frederik Hoppe; Claudio Mayrink Verdun; Hannah Laus; Felix Krahmer; Holger Rauhut (MLSP, 2023)

Fast, blind, and accurate: Tuning-free sparse regression with global linear convergence. - Claudio Mayrink Verdun; Oleh Melnyk; Felix Krahmer; Peter Jung 001 (COLT, 2024)

Imaging with Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images. - Frederik Hoppe; Claudio Mayrink Verdun; Hannah Laus; Sebastian Endt; Marion I. Menzel; Felix Krahmer; Holger Rauhut (ECCV (78), 2024)

Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning. - Frederik Hoppe; Claudio Mayrink Verdun; Hannah Laus; Felix Krahmer; Holger Rauhut (NeurIPS, 2024)

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models. - Adam Karvonen; Benjamin Wright; Can Rager; Rico Angell; Jannik Brinkmann; Logan Smith; Claudio Mayrink Verdun; David Bau; Samuel Marks (NeurIPS, 2024)

Multi-Group Proportional Representation in Retrieval. - Alex Oesterling; Claudio Mayrink Verdun; Alexander Glynn; Carol Xuan Long; Lucas Monteiro Paes; Sajani Vithana; Martina Cardone; Flávio P. Calmon (NeurIPS, 2024)

Multi-Group Proportional Representation. - Alex Oesterling; Claudio Mayrink Verdun; Carol Xuan Long; Alexander Glynn; Lucas Monteiro Paes; Sajani Vithana; Martina Cardone; Flávio P. Calmon (CoRR, 2024)

With or Without Replacement? Improving Confidence in Fourier Imaging. - Frederik Hoppe; Claudio Mayrink Verdun; Felix Krahmer; Marion I. Menzel; Holger Rauhut (CoRR, 2024)

Get rid of your constraints and reparametrize: A study in NNLS and implicit bias. - Hung-Hsu Chou; Johannes Maly; Claudio Mayrink Verdun; Bernardo Freitas Paulo da Costa; Heudson Mirandola (AISTATS, 2025)

Multi-Group Proportional Representations for Text-to-Image Models. - Sangwon Jung; Alex Oesterling; Claudio Mayrink Verdun; Sajani Vithana; Taesup Moon; Flávio P. Calmon (CVPR, 2025)

AI Alignment at Your Discretion. - Maarten Buyl; Hadi Khalaf; Claudio Mayrink Verdun; Lucas Monteiro Paes; Caio Cesar Vieira Machado; Flávio du Pin Calmon (FAccT, 2025)

Soft Best-of-n Sampling for Model Alignment. - Claudio Mayrink Verdun; Alex Oesterling; Himabindu Lakkaraju; Flávio P. Calmon (CoRR, 2025)

Optimized Couplings for Watermarking Large Language Models. - Dor Tsur; Carol Xuan Long; Claudio Mayrink Verdun; Hsiang Hsu; Haim H. Permuter; Flávio P. Calmon (CoRR, 2025)

GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection. - Mariia Seleznova; Hung-Hsu Chou; Claudio Mayrink Verdun; Gitta Kutyniok (CoRR, 2025)

Multi-Group Proportional Representation for Text-to-Image Models. - Sangwon Jung; Alex Oesterling; Claudio Mayrink Verdun; Sajani Vithana; Taesup Moon; Flávio P. Calmon (CoRR, 2025)

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions. - Dor Tsur; Carol Xuan Long; Claudio Mayrink Verdun; Hsiang Hsu; Chen-Fu Chen; Haim H. Permuter; Sajani Vithana; Flávio P. Calmon (CoRR, 2025)

Inference-Time Reward Hacking in Large Language Models. - Hadi Khalaf; Claudio Mayrink Verdun; Alex Oesterling; Himabindu Lakkaraju; Flávio du Pin Calmon (CoRR, 2025)

ProofCompass: Enhancing Specialized Provers with LLM Guidance. - Nicolas Wischermann; Claudio Mayrink Verdun; Gabriel Poesia; Francesco Noseda (CoRR, 2025)