David Holzmüller

30 papers A* 5Journal 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Variational Estimator for L CoRR Eugene Berta, Sacha Braun, David Holzmüller, Francis R. Bach, Michael I. Jordan
2026	J	`jnl`	TabICLv2: A better, faster, scalable, and open tabular foundation model. CoRR Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan
2025	A*	`conf`	Active Learning for Neural PDE Solvers. ICLR Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert
2025	J	`jnl`	Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks. CoRR David Holzmüller, Max Schölpple
2025	J	`jnl`	Conditional Coverage Diagnostics for Conformal Prediction. CoRR Sacha Braun, David Holzmüller, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. J. Mach. Learn. Res. David Holzmüller, Francis Bach
2025	J	`jnl`	LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators. CoRR Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
2025	J	`jnl`	LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators. Trans. Mach. Learn. Res. Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
2025	J	`jnl`	Rethinking Early Stopping: Refine, Then Calibrate. CoRR Eugene Berta, David Holzmüller, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Structured Matrix Scaling for Multi-Class Calibration. CoRR Eugene Berta, David Holzmüller, Michael I. Jordan, Francis Bach
2025	J	`jnl`	TabArena: A Living Benchmark for Machine Learning on Tabular Data. CoRR Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter
2025	A*	`conf`	TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. ICML Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan
2025	J	`jnl`	TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. CoRR Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan
2025	J	`jnl`	xRFM: Accurate, scalable, and interpretable feature learning models for tabular data. CoRR Daniel Beaglehole, David Holzmüller, Adityanarayanan Radhakrishnan, Mikhail Belkin
2024	J	`jnl`	Active Learning for Neural PDE Solvers. CoRR Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert
2024	J	`jnl`	Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data. CoRR David Holzmüller, Léo Grinsztajn, Ingo Steinwart
2024	A*	`conf`	Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. NeurIPS David Holzmüller, Léo Grinsztajn, Ingo Steinwart
2023	J	`jnl`	A Framework and Benchmark for Deep Batch Active Learning for Regression. J. Mach. Learn. Res. David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
2023	J	`jnl`	Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation. CoRR David Holzmüller, Francis R. Bach
2023	A*	`conf`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2023	J	`jnl`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2023	—	`—`	Regression from linear models to neural networks: double descent, active learning, and sampling. David Holzmüller
2022	J	`jnl`	A Framework and Benchmark for Deep Batch Active Learning for Regression. CoRR David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
2022	J	`jnl`	Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. J. Mach. Learn. Res. David Holzmüller, Ingo Steinwart
2021	A*	`conf`	On the Universality of the Double Descent Peak in Ridgeless Regression. ICLR David Holzmüller
2020	J	`jnl`	Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. Frontiers Robotics AI Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, Syn Schmitt
2020	J	`jnl`	On the Universality of the Double Descent Peak in Ridgeless Regression. CoRR David Holzmüller
2020	J	`jnl`	Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. CoRR David Holzmüller, Ingo Steinwart
2017	J	`jnl`	Efficient Neighbor-Finding on Space-Filling Curves. CoRR David Holzmüller
2017	J	`jnl`	Improved Approximation Schemes for the Restricted Shortest Path Problem. CoRR David Holzmüller