3 papers
cs.AI2026
GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer
Junmo Cho, Suhan Kim, Sangjune An +5
Finding effective prompts for language models (LMs) is critical yet notoriously difficult: the prompt space is combinatorially large, rewards are sparse due to expensive target-LM…
cs.LG2025
Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim +5
In this paper, we address the problem of \emph{cost-sensitive} hyperparameter optimization (HPO) built upon freeze-thaw Bayesian optimization (BO). Specifically, we assume a scenar…
cs.LG2025
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
Dongwoo Lee, Dong Bok Lee, Steven Adriaensen +5
Scaling has been a major driver of recent advancements in deep learning. Numerous empirical studies have found that scaling laws often follow the power-law and proposed several var…