David Holzmüller

I am a postdoc at INRIA Paris, working with Francis Bach and Gaël Varoquaux.

E-Mail: firstname.lastname@inria.fr (replace ü by u)

Research interests

I am currently interested in supervised learning, active learning, and uncertainty quantification with neural networks. I am interested in improving them on tabular data, but have also worked on interatomic potentials together with Viktor Zaverkin and am generally interested in AI4Science. Previously, I have also worked on theory of neural networks (neural tangent kernels, double descent, benign overfitting) and non-log-concave sampling, as well as other topics.

Papers

ML for Tabular Data

David Holzmüller, Léo Grinsztajn, and Ingo Steinwart, Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data, 2024. https://arxiv.org/abs/2407.04491

Active Learning

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart, A Framework and Benchmark for Deep Batch Active Learning for Regression, Journal of Machine Learning Research, 2023. https://arxiv.org/abs/2203.09410

Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert, Active Learning for Neural PDE Solvers, 2024. https://arxiv.org/abs/2408.01536

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner, Exploring chemical and conformational spaces by batch mode deep active learning, Digital Discovery, 2022. https://doi.org/10.1039/D2DD00034B

Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, and Johannes Kästner, Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials, npj Computational Materials, 2024. https://www.nature.com/articles/s41524-024-01254-1

NN Theory

Moritz Haas*, David Holzmüller*, Ulrike von Luxburg, and Ingo Steinwart, Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension, Neural Information Processing Systems, 2023. https://proceedings.neurips.cc/paper_files/paper/2023/hash/421f83663c02cdaec8c3c38337709989-Abstract-Conference.html

David Holzmüller, On the Universality of the Double Descent Peak in Ridgeless Regression, International Conference on Learning Representations, 2021. https://openreview.net/forum?id=0IO5VdnSAaH

David Holzmüller, Ingo Steinwart, Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, Journal of Machine Learning Research, 2022. https://jmlr.org/papers/v23/20-830.html

Sampling Theory

David Holzmüller and Francis Bach, Convergence rates for non-log-concave sampling and log-partition estimation, 2023. https://arxiv.org/abs/2303.03237

Other atomistic ML

Viktor Zaverkin*, David Holzmüller*, Ingo Steinwart, and Johannes Kästner, Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments, J. Chem. Theory Comput. 17, 6658–6670, 2021. https://arxiv.org/abs/2109.09569

Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, and Johannes Kästner, Transfer learning for chemically accurate interatomic neural network potentials, 2022. https://arxiv.org/abs/2212.03916

Viktor Zaverkin, David Holzmüller, Robin Schuldt, and Johannes Kästner, Predicting properties of periodic systems from cluster data: A case study of liquid water, J. Chem. Phys. 156, 114103, 2022. https://aip.scitation.org/doi/full/10.1063/5.0078983

Other

David Holzmüller and Dirk Pflüger, Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework, 2021. In: Bungartz, HJ., Garcke, J., Pflüger, D. (eds) Sparse Grids and Applications - Munich 2018. Lecture Notes in Computational Science and Engineering, vol 144. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-81362-8_4

Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, Syn Schmitt, Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking, 2020. https://www.frontiersin.org/articles/10.3389/frobt.2020.00077/full

David Holzmüller, Improved Approximation Schemes for the Restricted Shortest Path Problem, 2017. https://arxiv.org/abs/1711.00284

David Holzmüller, Efficient Neighbor-Finding on Space-Filling Curves, 2017. https://arxiv.org/abs/1710.06384

Newest Blog Posts

Short CV

• since 2023: Postdoc at INRIA Paris, co-advised by Francis Bach and Gaël Varoquaux
• April 2022 - July 2022: Research visit at INRIA Paris, Francis Bach
• 2020 - 2023: PhD student at University of Stuttgart, supervised by Ingo Steinwart
• 2016 - 2019: M.Sc. Computer Science, University of Stuttgart
• 2015 - 2019: B.Sc. Mathematics, University of Stuttgart
• 2013 - 2016: B.Sc. Computer Science, University of Stuttgart

Talk Topics:

• Introduction to ML for tabular data (various versions)
• Deep Batch Active Learning for Regression
• Generalization Theory of Linearized Neural Networks (INRIA Paris, University of Twente)