A portrait of the website author, David Holzmüller

David Holzmüller

I am a starting researcher in the SODA team at INRIA Saclay.

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

Research interests

I am currently working in machine learning for tabular data and uncertainty quantification. With my coauthors, I have recently introduced strong tabular classification and regression methods (TabICLv2, TabICL, RealMLP, xRFM) as well as the TabArena benchmark. Additionally, I am interested in active learning and expanding the scope of meta-learned tabular foundation models. Previously, I have worked on various other topics including interatomic potentials, machine learning theory (neural tangent kernels, double descent, benign overfitting, non-log-concave sampling), and other things.

Talks

Tabular data

Active learning

  • Active Learning for Science (AMLAB Seminar, 2025, slides)

Theory

  • Generalization theory of linearized neural networks (MPI MIS + UCLA seminar, slides and video)

Papers

ML for Tabular Data

Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan, TabICLv2: A better, faster, scalable, and open tabular foundation model, arXiv:2602.11139, 2026. https://arxiv.org/abs/2602.11139

Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan, TabICL: A Tabular Foundation Model for In-Context Learning on Large Data, International Conference on Machine Learning, 2025. https://arxiv.org/abs/2502.05564

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

Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, and Frank Hutter, TabArena: A Living Benchmark for Machine Learning on Tabular Data, Neural Information Processing Systems (spotlight), 2025. https://openreview.net/forum?id=jZqCqpCLdU

Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, and Mikhail Belkin, xRFM: Accurate, scalable, and interpretable feature learning models for tabular data, International Conference on Learning Representations, 2026. https://openreview.net/forum?id=wHuVdpnUFp

Uncertainty Quantification

Sacha Braun, David Holzmüller, Michael I. Jordan, and Francis Bach, Conditional Coverage Diagnostics for Conformal Prediction, arXiv:2512.11779, 2025. https://arxiv.org/abs/2512.11779

Eugène Berta, David Holzmüller, Michael I. Jordan, and Francis Bach, Structured Matrix Scaling for Multi-Class Calibration, Artificial Intelligence and Statistics, 2026. https://arxiv.org/abs/2511.03685

Eugène Berta, David Holzmüller, Michael I. Jordan, and Francis Bach, Rethinking Early Stopping: Refine, Then Calibrate, arXiv:2501.19195, 2025. https://arxiv.org/abs/2501.19195

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://jmlr.org/papers/v24/22-0937.html

Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert, Active Learning for Neural PDE Solvers, International Conference on Learning Representations, 2025. https://openreview.net/forum?id=x4ZmQaumRg

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

David Holzmüller* and Max Schölpple*, Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks, Artificial Intelligence and Statistics, 2026. https://arxiv.org/abs/2506.22429

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, Journal of Machine Learning Research, 2025. https://jmlr.org/papers/v26/24-1494.html

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

Marimuthu Kalimuthu, David Holzmüller, and Mathias Niepert, LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators, Transactions on Machine Learning Research, 2025. https://openreview.net/forum?id=MQ1dRdHTpi

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

Short CV

  • since 2025: Starting researcher at INRIA Saclay, SODA team
  • 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

Blog Posts