David Holzmüller

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Ingo SteinwartUniversity of StuttgartVerified email at mathematik.uni-stuttgart.de
Viktor ZaverkinNEC Laboratories EuropeVerified email at neclab.eu
Johannes KästnerUniversity of StuttgartVerified email at theochem.uni-stuttgart.de
Mathias NiepertUniversity of Stuttgart & NEC Labs EuropeVerified email at ki.uni-stuttgart.de
Luca BonfirraroPhD Student, Max Planck Institute FKF - StuttgartVerified email at fkf.mpg.de
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Daniel HaeufleUniversity of Tübingen, GermanyVerified email at uni-tuebingen.de
Isabell WochnerHeidelberg UniversityVerified email at ziti.uni-heidelberg.de
Federico ErricaSenior Research Scientist, NECVerified email at neclab.eu
Henrik ChristiansenNEC Laboratories EuropeVerified email at neclab.eu
Francesco AlesianiNEC Laboratories EuropeVerified email at neclab.eu
Léo GRINSZTAJNPhD Student, INRIAVerified email at inria.fr
Moritz HaasPhD Student, University of TübingenVerified email at uni-tuebingen.de
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Daniel MusekampUniversity of StuttgartVerified email at ki.uni-stuttgart.de
Dirk PflügerUniversity of StuttgartVerified email at ipvs.uni-stuttgart.de
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Eugène BertaPhD Student, INRIA - Ecole Normale SupérieureVerified email at inria.fr
Marine Le MorvanResearch scientist, INRIA SaclayVerified email at inria.fr
Jingang QUCentre Inria de Saclay, SODAVerified email at inria.fr

David Holzmüller

Postdoc, INRIA

Verified email at inria.fr - Homepage

Deep Learning Machine Learning Computational Chemistry Sampling


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A Framework and Benchmark for Deep Batch Active Learning for Regression D Holzmüller, V Zaverkin, J Kästner, I Steinwart Journal of Machine Learning Research 24 (164), 1-81, 2023	41	2023
Transfer learning for chemically accurate interatomic neural network potentials V Zaverkin, D Holzmüller, L Bonfirraro, J Kästner Physical Chemistry Chemical Physics 25 (7), 5383-5396, 2023	35	2023
Exploring chemical and conformational spaces by batch mode deep active learning V Zaverkin, D Holzmüller, I Steinwart, J Kästner Digital Discovery 1 (5), 605-620, 2022	34	2022
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments V Zaverkin, D Holzmüller, I Steinwart, J Kästner Journal of Chemical Theory and Computation 17 (10), 6658-6670, 2021	34	2021
Predicting properties of periodic systems from cluster data: A case study of liquid water V Zaverkin, D Holzmüller, R Schuldt, J Kästner The Journal of Chemical Physics 156 (11), 114103, 2022	26	2022
Muscles reduce neuronal information load: quantification of control effort in biological vs. robotic pointing and walking DFB Haeufle, I Wochner, D Holzmüller, D Driess, M Günther, S Schmitt Frontiers in Robotics and AI, 77, 2020	22	2020
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ... npj Computational Materials, 2024	20	2024
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension M Haas, D Holzmüller, U von Luxburg, I Steinwart NeurIPS 2023, 2023	19	2023
On the Universality of the Double Descent Peak in Ridgeless Regression D Holzmüller International Conference on Learning Representations 2021, 2020	18	2020
Efficient Neighbor-Finding on Space-Filling Curves D Holzmüller arXiv preprint arXiv:1710.06384, 2017	12	2017
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation D Holzmüller, F Bach arXiv preprint arXiv:2303.03237, 2023	11	2023
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent D Holzmüller, I Steinwart Journal of Machine Learning Research 23 (181), 1-82, 2022	11	2022
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data D Holzmüller, L Grinsztajn, I Steinwart NeurIPS 2024, 2024	8	2024
Improved approximation schemes for the restricted shortest path problem D Holzmüller arXiv preprint arXiv:1711.00284, 2017	4	2017
Active Learning for Neural PDE Solvers D Musekamp, M Kalimuthu, D Holzmüller, M Takamoto, M Niepert arXiv preprint arXiv:2408.01536, 2024	3	2024
Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework D Holzmüller, D Pflüger Sparse Grids and Applications-Munich 2018, 69-100, 2021	1	2021
Convergence Analysis of Neural Networks D Holzmüller University of Stuttgart, 2019	1	2019
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data J Qu, D Holzmüller, G Varoquaux, ML Morvan arXiv preprint arXiv:2502.05564, 2025		2025
Rethinking Early Stopping: Refine, Then Calibrate E Berta, D Holzmüller, MI Jordan, F Bach arXiv preprint arXiv:2501.19195, 2025		2025
LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators M Kalimuthu, D Holzmüller, M Niepert ICLR 2025 Workshop on Machine Learning Multiscale Processes, 2025		2025

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