Adaptive Knowledge Transfer on Evolving Domains

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we study the dynamic transfer learning problem involving adaptive knowledge transfer from a static source domain to a time evolving target domain. One major challenge is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time. To address this challenge, we derive a generic error bound on the current target domain with flexible domain discrepancy measures. Moreover, we propose a label-informed C-divergence to measure the shift of joint data distributions (over input features and output labels) across domains. The resulting tighter error bound with C-divergence motivates us to develop a novel dynamic transfer learning algorithm TransLATE. Empirical results on various data sets confirm the effectiveness of our proposed algorithm in modeling the time evolving target domain.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665480451
StatePublished - 2022
Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
Duration: Dec 17 2022Dec 20 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022


Conference2022 IEEE International Conference on Big Data, Big Data 2022


  • distribution discrepancy
  • evolving domain
  • transfer learning

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization


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