no code implementations • 14 Feb 2024 • Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang
Previous studies have established a regret bound of $O(T^{3/4}+d^{1/3}T^{2/3})$ for this problem, where $d$ is the maximum delay, by simply feeding delayed loss values to the classical bandit gradient descent (BGD) algorithm.
no code implementations • 29 May 2023 • Yucheng Liao, Yuanyu Wan, Chang Yao, Mingli Song
We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently.
no code implementations • 20 May 2023 • Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang
Despite its simplicity, our novel analysis shows that the dynamic regret of DOGD can be automatically bounded by $O(\sqrt{\bar{d}T}(P_T+1))$ under mild assumptions, and $O(\sqrt{dT}(P_T+1))$ in the worst case, where $\bar{d}$ and $d$ denote the average and maximum delay respectively, $T$ is the time horizon, and $P_T$ is the path length of comparators.
no code implementations • 11 Apr 2022 • Yuanyu Wan, Yibo Wang, Chang Yao, Wei-Wei Tu, Lijun Zhang
Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints.
no code implementations • 20 Oct 2020 • Chang Yao, Jingyu Tang, Menghan Hu, Yue Wu, Wenyi Guo, Qingli Li, Xiao-Ping Zhang
Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe.
no code implementations • 25 Sep 2019 • Lei Zhu, Wei Wang, Mei Hui Zhang, Beng Chin Ooi, Chang Yao
State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains.