3 papers
cs.LG2026
Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task
Alicia Curth, Rachel Lawrence, Sushrut Karmalkar +1
We investigate whether transformers use their depth adaptively across tasks of increasing difficulty. Using a controlled multi-hop relational reasoning task based on family stories…
cs.LG2024
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
Alan Jeffares, Alicia Curth, Mihaela van der Schaar
Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate mo…
cs.LG2024
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…