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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…
math.ST2025
Central Limit Theorems for Transition Probabilities of Controlled Markov Chains
Ziwei Su, Imon Banerjee, Diego Klabjan
We develop a central limit theorem (CLT) for a non-parametric estimator of the transition matrices in controlled Markov chains (CMCs) with finite state-action spaces. Our results e…
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…