Chris Kolb

About

I am a PhD student at the Chair of Statistical Learning and Data Science (SLDS) in affiliation with the Munich Center for Machine Learning (MCML) since 2021.

Before that, I obtained a Bachelor's degree (B.Sc.) in Economics from Goethe University Frankfurt and a joint Master's degree (M.Sc.) in Statistics from Humboldt University of Berlin, Technical University Berlin, Free University Berlin, and Charité Berlin.

My research focus is two-fold: Currently, I'm highly interested in exact differentiable optimization for non-smooth objectives using overparametrization, particularly in the context of sparsity in deep learning. My second area of research is the amalgamation of classical statistical models and machine learning concepts to create interpretable and well-founded, but highly flexible and scalable hybrid approaches.

Furthermore, I am part of the Data Science Group chaired by Prof. David Rügamer.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

chris.kolb [at] stat.uni-muenchen.de

Research Interests

Awards

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References

  1. Kolb C, Müller CL, Bischl B, Rügamer D (2023) Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. arXiv preprint arXiv:2307.03571.
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  2. Kolb C, Bischl B, Müller CL, Rügamer D (2023) Sparse Modality Regression. Proceedings of the 37th International Workshop on Statistical Modelling, IWSM 2023,
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  3. Rügamer D, Kolb C, Klein N (2022) Semi-Structured Distributional Regression. The American Statistician.
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  4. Rügamer D, Kolb C, Fritz C, Pfisterer F, Kopper P, Bischl B, Shen R, Bukas C, Sousa LB de Andrade e, Thalmeier D, Baumann P, Kook L, Klein N, Müller CL (2022) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software.
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