4 papers · 1 filter
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
Algorithms increasingly operate within complex physical, social, and engineering systems where they are exposed to disturbances, noise, and interconnections with other dynamical sy…
A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications
Klaus-Rudolf Kladny, Bernhard Schölkopf, Lisa Koch +2
Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (C…
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining
Melis Ilayda Bal, Volkan Cevher, Michael Muehlebach
Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introduce Efficient Selective Language Model…
Adversarial Training for Defense Against Label Poisoning Attacks
Melis Ilayda Bal, Volkan Cevher, Michael Muehlebach
As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become…