2 citations · 3 across the 6 of their papers we have counts for
8 papers · 1 filter
Federated Binary Matrix Factorization using Proximal Optimization
Sebastian Dalleiger, Jilles Vreeken, Michael Kamp
Identifying informative components in binary data is an essential task in many research areas, including life sciences, social sciences, and recommendation systems. Boolean matrix…
Landscaping Linear Mode Connectivity
Sidak Pal Singh, Linara Adilova, Michael Kamp +3
The presence of linear paths in parameter space between two different network solutions in certain cases, i.e., linear mode connectivity (LMC), has garnered interest from both theo…
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Fan Yang, Pierre Le Bodic, Michael Kamp +1
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to li…
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks
Henning Petzka, Linara Adilova, Michael Kamp +1
The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions…
Communication-Efficient Distributed Online Learning with Kernels
Michael Kamp, Sebastian Bothe, Mario Boley +1
We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that repre…
Corresponding Projections for Orphan Screening
Sven Giesselbach, Katrin Ullrich, Michael Kamp +2
We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of c…