3 papers
cs.LG2025
A Trainable Optimizer
Ruiqi Wang, Diego Klabjan
The concept of learning to optimize involves utilizing a trainable optimization strategy rather than relying on manually defined full gradient estimations such as ADAM. We present…
cs.LG2025
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
Taejong Joo, Diego Klabjan
Transformers have demonstrated remarkable in-context learning (ICL) capabilities, adapting to new tasks by simply conditioning on demonstrations without parameter updates. Compelli…
stat.ME2024
Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization
Ziwei Su, Diego Klabjan
Stochastic simulation models are generative models that mimic complex systems to help with decision-making. The reliability of these models heavily depends on well-calibrated input…