Search Results for author: Ruizhi Pu

Found 5 papers, 0 papers with code

Generalizing across Temporal Domains with Koopman Operators

no code implementations12 Feb 2024 Qiuhao Zeng, Wei Wang, Fan Zhou, Gezheng Xu, Ruizhi Pu, Changjian Shui, Christian Gagne, Shichun Yang, Boyu Wang, Charles X. Ling

By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.

Domain Generalization Generalization Bounds

When Source-Free Domain Adaptation Meets Learning with Noisy Labels

no code implementations31 Jan 2023 Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang

We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA.

Learning with noisy labels Source-Free Domain Adaptation

Evolving Domain Generalization

no code implementations31 May 2022 William Wei Wang, Gezheng Xu, Ruizhi Pu, Jiaqi Li, Fan Zhou, Changjian Shui, Charles Ling, Christian Gagné, Boyu Wang

Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.

Evolving Domain Generalization Meta-Learning

Directional Domain Generalization

no code implementations29 Sep 2021 Wei Wang, Jiaqi Li, Ruizhi Pu, Gezheng Xu, Fan Zhou, Changjian Shui, Charles Ling, Boyu Wang

Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.

Domain Generalization Meta-Learning +1

YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker

no code implementations14 Sep 2021 Ruizhi Pu, Xinyu Zhang, Ruofei Lai, Zikai Guo, Yinxia Zhang, Hao Jiang, Yongkang Wu, Yantao Jia, Zhicheng Dou, Zhao Cao

Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.

Document Ranking Information Retrieval +1

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