Efficient Neighbor-Finding on Space-Filling Curves. - David Holzmüller (CoRR, 2017)

Improved Approximation Schemes for the Restricted Shortest Path Problem. - David Holzmüller (CoRR, 2017)

Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. - Daniel F. B. Haeufle; Isabell Wochner; David Holzmüller; Danny Driess; Michael Günther 001; Syn Schmitt (Frontiers Robotics AI, 2020)

On the Universality of the Double Descent Peak in Ridgeless Regression. - David Holzmüller (ICLR, 2021)

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. - Viktor Zaverkin; David Holzmüller; Ingo Steinwart; Johannes Kästner (DaRUS, 2021)

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. - David Holzmüller; Ingo Steinwart (J. Mach. Learn. Res., 2022)

Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. - David Holzmüller; Ingo Steinwart (DaRUS, 2022)

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. - Moritz Haas; David Holzmüller; Ulrike von Luxburg; Ingo Steinwart (NeurIPS, 2023)

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. - David Holzmüller; Francis R. Bach (CoRR, 2023)

A Framework and Benchmark for Deep Batch Active Learning for Regression. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (J. Mach. Learn. Res., 2023)

Regression from linear models to neural networks: double descent, active learning, and sampling. - David Holzmüller (2023)

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (DaRUS, 2023)

Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. - David Holzmüller; Léo Grinsztajn; Ingo Steinwart (NeurIPS, 2024)

Active Learning for Neural PDE Solvers. - Daniel Musekamp; Marimuthu Kalimuthu; David Holzmüller; Makoto Takamoto; Mathias Niepert (ICLR, 2025)

Rethinking Early Stopping: Refine, Then Calibrate. - Eugene Berta; David Holzmüller; Michael I. Jordan; Francis R. Bach (CoRR, 2025)

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. - Jingang Qu; David Holzmüller; Gaël Varoquaux; Marine Le Morvan (CoRR, 2025)

LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators. - Marimuthu Kalimuthu; David Holzmüller; Mathias Niepert (CoRR, 2025)

TabArena: A Living Benchmark for Machine Learning on Tabular Data. - Nick Erickson; Lennart Purucker; Andrej Tschalzev; David Holzmüller; Prateek Mutalik Desai; David Salinas; Frank Hutter (CoRR, 2025)

Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks. - David Holzmüller; Max Schölpple (CoRR, 2025)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data. - Daniel Beaglehole; David Holzmüller; Adityanarayanan Radhakrishnan; Mikhail Belkin (CoRR, 2025)