71 citations · 201 across the 24 of their papers we have counts for
28 papers
Rethinking Routing Exploration in Modern Mixture-of-Experts
Bingcong Li, Hao Ma, Michael Muehlebach +2
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained exper…
Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models
Haolong Li, Guner Dilsad Er, Michael Muehlebach +1
Vision-language-action models (VLAs) have emerged as a promising paradigm for general-purpose robot learning, with performance improving as models and datasets scale. Scaling robot…
Electromagnetic Micro-Guidewire Control in Large Workspaces
Jasan Zughaibi, Elia Jaggy, Valentin Gantenbein +9
Electromagnetic navigation requires sufficient actuation at clinically relevant distances due to limited magnetic volumes and coil currents. We combine real-time pose feedback with…
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)…