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cs.LG2026
Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
Anastasis Kratsios, A. Martina Neuman, Philipp Petersen
We compare in-context learning with fixed queries and agentic learning with adaptive queries for uniform approximation of task families. We consider two settings: an unrestricted r…
cs.LG2025
Consistency of augmentation graph and network approximability in contrastive learning
Chenghui Li, A. Martina Neuman
Contrastive learning leverages data augmentation to develop feature representation without relying on large labeled datasets. However, despite its empirical success, the theoretica…
cs.LG2024
Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces
Anastasis Kratsios, A. Martina Neuman, Gudmund Pammer
Machine learning models with inputs in a Euclidean space , when implemented on digital computers, generalize, and their generalization gap converges to at a rate…