1 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training
Peng Cui, Boyao Yang, Jun Zhu
Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathematical reasoning in Large Language Models (LLMs), yet prevailing techniques such…
Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
Peng Cui, Boyao Yang, Jun Zhu
Reinforcement learning post-training has substantially improved the reasoning accuracy of vision-language models, yet the resulting policies remain poorly calibrated. Terminal corr…
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Ying Liu, Peng Cui, Wenbo Hu +1
Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the…
Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting
Guande He, Peng Cui, Jianfei Chen +2
Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-…
Heterogeneous Multi-Task Gaussian Cox Processes
Feng Zhou, Quyu Kong, Zhijie Deng +3
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via mu…