David Holzmüller

According to our database, David Holzmüller authored at least 19 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks.

David Holzmüller, Max Schölpple

CoRR, June, 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, June, 2025

LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators.

Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert

CoRR, April, 2025

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data.

Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan

CoRR, February, 2025

Rethinking Early Stopping: Refine, Then Calibrate.

Eugene Berta, David Holzmüller, Michael I. Jordan, Francis R. Bach

CoRR, January, 2025

Active Learning for Neural PDE Solvers.

Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Better by default: Strong pre-tuned MLPs and boosted trees on tabular data.

David Holzmüller, Léo Grinsztajn, Ingo Steinwart

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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

Dataset, April, 2023

Regression from linear models to neural networks: double descent, active learning, and sampling.

David Holzmüller

PhD thesis, 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

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation.

David Holzmüller, Francis R. Bach

CoRR, 2023

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension.

Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent.

David Holzmüller, Ingo Steinwart

Dataset, June, 2022

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent.

David Holzmüller, Ingo Steinwart

J. Mach. Learn. Res., 2022

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

Dataset, October, 2021

On the Universality of the Double Descent Peak in Ridgeless Regression.

David Holzmüller

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

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, Syn Schmitt

Frontiers Robotics AI, 2020

2017

Improved Approximation Schemes for the Restricted Shortest Path Problem.

David Holzmüller

CoRR, 2017

Efficient Neighbor-Finding on Space-Filling Curves.

David Holzmüller

CoRR, 2017