4 papers
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Shengtian Yang, Yu Li, Shuo He +4
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing…
Variational Rectification Inference for Learning with Noisy Labels
Haoliang Sun, Qi Wei, Lei Feng +4
Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re…
FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning
Ziquan Wang, Haobo Wang, Ke Chen +2
Machine Learning often involves various imprecise labels, leading to diverse weakly supervised settings. While recent methods aim for universal handling, they usually suffer from c…
Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
Qi Wei, Lei Feng, Haobo Wang +1
Learning with noisy labels aims to ensure model generalization given a label-corrupted training set. The sample selection strategy achieves promising performance by selecting a lab…