Search Results for author: Chang Yao

Found 8 papers, 2 papers with code

Improved Regret for Bandit Convex Optimization with Delayed Feedback

no code implementations14 Feb 2024 Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang

We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay.

Blocking

Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation

1 code implementation CVPR 2024 Lin Long, Haobo Wang, Zhijie Jiang, Lei Feng, Chang Yao, Gang Chen, Junbo Zhao

To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations.

Binary Classification Contrastive Learning +1

Improved Projection-free Online Continuous Submodular Maximization

no code implementations29 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.

Blocking

Non-stationary Online Convex Optimization with Arbitrary Delays

no code implementations20 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.

Projection-free Online Learning with Arbitrary Delays

no code implementations11 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.

Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

no code implementations20 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.

Distribution Matching Prototypical Network for Unsupervised Domain Adaptation

no code implementations25 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.

Unsupervised Domain Adaptation

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