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Creators/Authors contains: "Bo Liu"

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  1. (, arXivorg)
    Accepted Manuscript Full Text Available
  2. (, arXivorg)
    Accepted Manuscript Full Text Available
  3. (, arXivorg)
    Accepted Manuscript Full Text Available
  4. (, arXivorg)
    Accepted Manuscript Full Text Available
  5. (, arXivorg)
    Accepted Manuscript Full Text Available
  6. (, European Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  7. (, Proceedings of The 1st Conference on Lifelong Learning Agents)
    Accepted Manuscript Full Text Available
  8. (, Proceedings of The 1st Conference on Lifelong Learning Agents)
    Accepted Manuscript Full Text Available
  9. (, NeurIPS 2021)
    Accepted Manuscript Full Text Available
  10. ; ; (, Advances in neural information processing systems)
    As intelligent agents become autonomous over longer periods of time, they may eventually be- come lifelong counterparts to specific people. If so, it may be common for a user to want the agent to master a task temporarily but later on to forget the task due to privacy concerns. How- ever enabling an agent to forget privately what the user specified without degrading the rest of the learned knowledge is a challenging problem. With the aim of addressing this challenge, this paper formalizes this continual learning and private unlearning (CLPU) problem. The pa- per further introduces a straightforward but exactly private solution, CLPU-DER++, as the first step towards solving the CLPU problem, along with a set of carefully designed benchmark prob- lems to evaluate the effectiveness of the proposed solution. 
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    Accepted Manuscript Full Text Available