Holzmüller, David

12 publications

ICLR 2025 Active Learning for Neural PDE Solvers Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

JMLR 2025 Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation David Holzmüller, Francis Bach

TMLR 2025 LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert

ICLRW 2025 LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert

NeurIPS 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

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

NeurIPSW 2024 Active Learning for Neural PDE Solvers Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

NeurIPS 2024 Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data David Holzmüller, Léo Grinsztajn, Ingo Steinwart

JMLR 2023 A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

NeurIPS 2023 Mind the Spikes: Benign Overfitting of Kernels and Neural Networks in Fixed Dimension Moritz Haas, David Holzmüller, Ulrike V. Luxburg, Ingo Steinwart

JMLR 2022 Training Two-Layer ReLU Networks with Gradient Descent Is Inconsistent David Holzmüller, Ingo Steinwart

ICLR 2021 On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller