Publications

See also my Google Scholar profile.

Liangzu Peng, Christian Kümmerle, René Vidal (2023). On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation. CVPR 2023, accepted.

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Christian Kümmerle, Mauro Maggioni, Sui Tang (2022). Learning Transition Operators From Sparse Space-Time Samples. arXiv:2212.00746.

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Liangzu Peng, Christian Kümmerle, René Vidal (2022). Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression. NeurIPS 2022, accepted.

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Felix Krahmer, Christian Kümmerle, Oleh Melnyk (2022). On the robustness of noise-blind low-rank recovery from rank-one measurements. Linear Algebra Appl., 652, 37-81.

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Olivier Guédon, Felix Krahmer, Christian Kümmerle, Shahar Mendelson, Holger Rauhut (2022). On the geometry of polytopes generated by heavy-tailed random vectors. Commun. Contemp. Math., 24(03), 2150056.

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Pedro Abdalla, Christian Kümmerle (2022). Dictionary-Sparse Recovery From Heavy-Tailed Measurements. Inf. Inference, 11(4), 1501–1526.

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Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger (2021). Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. NeurIPS 2021 (Spotlight, top 3% of submitted papers).

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

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Christian Kümmerle, Claudio Mayrink Verdun (2020). Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method. In Workshop on Beyond First Order Methods in ML Systems at ICML 2020.

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Christian Kümmerle (2019). Understanding and Enhancing Data Recovery Algorithms - From Noise-Blind Sparse Recovery to Reweighted Methods for Low-Rank Matrix Optimization. Ph.D. Dissertation, TU Munich, 2019.

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Christian Kümmerle, Claudio Mayrink Verdun (2019). Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares. In SampTA 2019, pp. 1-5.

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Christian Kümmerle, Claudio Mayrink Verdun (2018). Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares. In Proceedings of iTWIST'18.

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Christian Kümmerle, Juliane Sigl (2018). Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery. J. Mach. Learn. Res., 19(47), 1815-1863.

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Felix Krahmer, Christian Kümmerle, Holger Rauhut (2018). A Quotient Property for Matrices with Heavy-Tailed Entries and its Application to Noise-Blind Compressed Sensing. arXiv:1806.04261.

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Christian Kümmerle, Juliane Sigl (2017). Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery. In SampTA 2017, pp. 489-493.

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