15 citations · 15 across the 2 of their papers we have counts for
4 papers
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…
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…
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…