41 citations · 74 across the 5 of their papers we have counts for
6 papers
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data
David Holzmüller, Léo Grinsztajn, Ingo Steinwart
For classification and regression on tabular data, the dominance of gradient-boosted decision trees (GBDTs) has recently been challenged by often much slower deep learning methods…
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
Moritz Haas, David Holzmüller, Ulrike von Luxburg +1
The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statis…
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart +1
We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformati…
A Framework and Benchmark for Deep Batch Active Learning for Regression
David Holzmüller, Viktor Zaverkin, Johannes Kästner +1
The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively…
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Viktor Zaverkin, David Holzmüller, Ingo Steinwart +1
Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simu…
Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning
Nicole Mücke, Ingo Steinwart
A common strategy to train deep neural networks (DNNs) is to use very large architectures and to train them until they (almost) achieve zero training error. Empirically observed go…