| Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery C Kümmerle, J Sigl Journal of Machine Learning Research 19 (47), 2018 | 32 | 2018 |
| Iteratively reweighted least squares for basis pursuit with global linear convergence rate C Kümmerle, C Mayrink Verdun, D Stöger Advances in Neural Information Processing Systems 34, 2873-2886, 2021 | 18* | 2021 |
| A scalable second order method for ill-conditioned matrix completion from few samples C Kümmerle, CM Verdun International Conference on Machine Learning, 5872-5883, 2021 | 15 | 2021 |
| On the convergence of IRLS and its variants in outlier-robust estimation L Peng, C Kümmerle, R Vidal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 12 | 2023 |
| Global linear and local superlinear convergence of IRLS for non-smooth robust regression L Peng, C Kümmerle, R Vidal Advances in neural information processing systems 35, 28972-28987, 2022 | 11 | 2022 |
| A quotient property for matrices with heavy-tailed entries and its application to noise-blind compressed sensing F Krahmer, C Kümmerle, H Rauhut arXiv preprint arXiv:1806.04261, 2018 | 9 | 2018 |
| On the geometry of polytopes generated by heavy-tailed random vectors O Guédon, F Krahmer, C Kümmerle, S Mendelson, H Rauhut Communications in Contemporary Mathematics 24 (03), 2150056, 2022 | 8 | 2022 |
| Understanding and Enhancing Data Recovery Algorithms C Kümmerle Technische Universität München, 2019 | 6 | 2019 |
| On the robustness of noise-blind low-rank recovery from rank-one measurements F Krahmer, C Kümmerle, O Melnyk Linear Algebra and its Applications 652, 37-81, 2022 | 5 | 2022 |
| Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method C Kümmerle, CM Verdun Workshop on "Beyond First Order Methods in Machine Learning Systems", ICML 2020, 2020 | 4 | 2020 |
| Completion of structured low-rank matrices via iteratively reweighted least squares C Kümmerle, CM Verdun 2019 13th international conference on sampling theory and applications …, 2019 | 4 | 2019 |
| Learning transition operators from sparse space-time samples C Kümmerle, M Maggioni, S Tang IEEE Transactions on Information Theory, 2024 | 2 | 2024 |
| Recovering simultaneously structured data via non-convex iteratively reweighted least squares C Kümmerle, J Maly Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares C Kümmerle, CM Verdun iTWIST '18: international Traveling Workshop on Interactions between low …, 2018 | 1 | 2018 |
| Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads AK Rahimian, MK Govind, S Maity, D Reilly, C Kümmerle, S Das, A Dutta arXiv preprint arXiv:2406.19391, 2024 | | 2024 |
| Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads A Khaleghi Rahimian, MK Govind, S Maity, D Reilly, C Kümmerle, S Das, ... arXiv e-prints, arXiv: 2406.19391, 2024 | | 2024 |
| UnitNorm: Rethinking Normalization for Transformers in Time Series N Huang, C Kümmerle, X Zhang arXiv preprint arXiv:2405.15903, 2024 | | 2024 |
| Dictionary-Sparse Recovery From Heavy-Tailed Measurements P Abdalla, C Kümmerle Information and Inference: A Journal of the IMA, 2022 | | 2022 |
| The Oracle of DLphi D Alfke, W Baines, J Blechschmidt, MJ Sarmina, A Drory, D Elbrächter, ... arXiv preprint arXiv:1901.05744, 2019 | | 2019 |
| On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation: Main Paper+ Supplementary Materials L Peng, C Kümmerle, R Vidal | | |
Claudio Mayrink VerdunHarvard UniversityVerified email at seas.harvard.edu
