3 papers
cs.LG2026
POP: Prior-Fitted First-Order Optimization Policies
Jan Kobiolka, Christian Frey, Gresa Shala +3
Gradient-based optimizers are highly sensitive to design choices in their adaptive learning rate mechanisms. To address this limitation, we introduce POP, a meta-learned Reinforcem…
cs.LG2026
Learning to Order: Task Sequencing as In-Context Optimization
Jan Kobiolka, Christian Frey, Arlind Kadra +2
Task sequencing (TS) is one of the core open problems in Deep Learning, arising in a plethora of real-world domains, from robotic assembly lines to autonomous driving. Unfortunatel…
cs.DB2024
Lightweight Correlation-Aware Table Compression
Mihail Stoian, Alexander van Renen, Jan Kobiolka +3
The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios.…