2 citations · 3 across the 5 of their papers we have counts for
5 papers
Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts
Klaus-Rudolf Kladny, Maximilian Mordig, Bernhard Schölkopf +1
Sparse Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token. Recent evidence suggests that performance improve…
Rethinking Easy-to-Hard: Limits of Curriculum Learning in Post-Training for Deductive Reasoning
Maximilian Mordig, Andreas Opedal, Weiyang Liu +1
Curriculum learning (CL), motivated by the intuition that learning in increasing order of difficulty should ease generalization, is commonly adopted both in pre-training and post-t…
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Frederike Lübeck, Jonas Wildberger, Frederik Träuble +4
Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the c…
Finding Stable Matchings in PhD Markets with Consistent Preferences and Cooperative Partners
Maximilian Mordig, Riccardo Della Vecchia, Nicolò Cesa-Bianchi +1
We introduce a new algorithm for finding stable matchings in multi-sided matching markets. Our setting is motivated by a PhD market of students, advisors, and co-advisors, and can…
Two-Sided Matching Markets in the ELLIS 2020 PhD Program
Maximilian Mordig, Riccardo Della Vecchia, Nicolò Cesa-Bianchi +1
The ELLIS PhD program is a European initiative that supports excellent young researchers by connecting them to leading researchers in AI. In particular, PhD students are supervised…