5 papers
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Xinzhe Yuan, Zimu Zeng, Zhuo Chen +6
Mixed-variable optimization in scientific discovery often requires exploring large combinatorial spaces under costly experimental evaluations, making sample?efficient Bayesian opti…
LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
Zhuo Chen, Xinzhe Yuan, Jianshu Zhang +8
The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). Howev…
Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
Jinzong Dong, Zhaohui Jiang, Bo Yang
Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General confidence calibration methods…
Proximal Action Replacement for Behavior Cloning Actor-Critic in Offline Reinforcement Learning
Jinzong Dong, Wei Huang, Jianshu Zhang +5
Offline reinforcement learning (RL), which optimizes policies using a previously collected static dataset, is an important branch of RL. A popular and promising approach is to regu…
Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling
Jinzong Dong, Zhaohui Jiang, Dong Pan +1
Confidence calibration of classification models is a technique to estimate the true posterior probability of the predicted class, which is critical for ensuring reliable decision-m…