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cs.LG2026
PENEX: AdaBoost-Inspired Neural Network Regularization
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach
AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes misclassified data points more severely than other loss functions like cross-ent…
cs.LG2025
Distributed Event-Based Learning via ADMM
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i)…
cs.LG2025
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach
Generative models lack rigorous statistical guarantees for their outputs and are therefore unreliable in safety-critical applications. In this work, we propose Sequential Conformal…