| 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 | 38 | 2023 |
| 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 |
| 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 | 30 | 2022 |
| 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 | 26 | 2023 |
| 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 | 21 | 2020 |
| On the Universality of the Double Descent Peak in Ridgeless Regression D Holzmüller International Conference on Learning Representations 2021, 2020 | 17 | 2020 |
| 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 | 16 | 2023 |
| Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ... npj Computational Materials, 2024 | 15 | 2024 |
| Efficient Neighbor-Finding on Space-Filling Curves D Holzmüller arXiv preprint arXiv:1710.06384, 2017 | 12 | 2017 |
| 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 |
| Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation D Holzmüller, F Bach arXiv preprint arXiv:2303.03237, 2023 | 9 | 2023 |
| Improved approximation schemes for the restricted shortest path problem D Holzmüller arXiv preprint arXiv:1711.00284, 2017 | 3 | 2017 |
| Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data D Holzmüller, L Grinsztajn, I Steinwart NeurIPS 2024, 2024 | 2 | 2024 |
| Active Learning for Neural PDE Solvers D Musekamp, M Kalimuthu, D Holzmüller, M Takamoto, M Niepert arXiv preprint arXiv:2408.01536, 2024 | 1 | 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 |
| Regression from linear models to neural networks: double descent, active learning, and sampling D Holzmüller University of Stuttgart, 2023 | | 2023 |
Viktor ZaverkinNEC Laboratories EuropeVerified email at neclab.eu