David Holzmüller

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart, A Framework and Benchmark for Deep Batch Active Learning for Regression, Journal of Machine Learning Research, 2023. https://jmlr.org/papers/v24/22-0937.html

Moritz Haas*, David Holzmüller*, Ulrike von Luxburg, and Ingo Steinwart, Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension, 2023. https://arxiv.org/abs/2305.14077

David Holzmüller and Francis Bach, Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation, 2023. https://arxiv.org/abs/2303.03237

Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, and Johannes Kästner, Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 25, 5383-5396, 2023. https://arxiv.org/abs/2212.03916

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner, Exploring chemical and conformational spaces by batch mode deep active learning, Digital Discovery, 2022. https://doi.org/10.1039/D2DD00034B

Viktor Zaverkin, David Holzmüller, Robin Schuldt, and Johannes Kästner, Predicting properties of periodic systems from cluster data: A case study of liquid water, J. Chem. Phys. 156, 114103, 2022. https://aip.scitation.org/doi/full/10.1063/5.0078983

David Holzmüller and Ingo Steinwart, Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent, Journal of Machine Learning Research, 2022. https://jmlr.org/papers/v23/20-830.html

David Holzmüller and Dirk Pflüger, Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework, 2021. In: Bungartz, HJ., Garcke, J., Pflüger, D. (eds) Sparse Grids and Applications - Munich 2018. Lecture Notes in Computational Science and Engineering, vol 144. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-81362-8_4

Viktor Zaverkin*, David Holzmüller*, Ingo Steinwart, and Johannes Kästner, Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments, J. Chem. Theory Comput. 17, 6658–6670, 2021. https://arxiv.org/abs/2109.09569

David Holzmüller, On the Universality of the Double Descent Peak in Ridgeless Regression, International Conference on Learning Representations, 2021. https://openreview.net/forum?id=0IO5VdnSAaH

Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, and Syn Schmitt, Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking, 2020. https://www.frontiersin.org/articles/10.3389/frobt.2020.00077/full

David Holzmüller, Improved Approximation Schemes for the Restricted Shortest Path Problem, 2017. https://arxiv.org/abs/1711.00284

David Holzmüller, Efficient Neighbor-Finding on Space-Filling Curves, 2017. https://arxiv.org/abs/1710.06384

Zum Porträt des Monats am Fachbereich, Februar 2020