71 citations · 74 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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)…
Online Learning under Adversarial Nonlinear Constraints
Pavel Kolev, Georg Martius, Michael Muehlebach
In many applications, learning systems are required to process continuous non-stationary data streams. We study this problem in an online learning framework and propose an algorith…
Adaptive Decision-Making with Constraints and Dependent Losses: Performance Guarantees and Applications to Online and Nonlinear Identification
Michael Muehlebach
We consider adaptive decision-making problems where an agent optimizes a cumulative performance objective by repeatedly choosing among a finite set of options. Compared to the clas…