4 papers · 1 filter
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…
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…
Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
Taejong Joo, Diego Klabjan
Self-training often falls short under distribution shifts due to an increased discrepancy between prediction confidence and actual accuracy. This typically necessitates computation…
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation
Taejong Joo, Diego Klabjan
Distribution shifts pose significant challenges for model calibration and model selection tasks in the unsupervised domain adaptation problem -- a scenario where the goal is to per…