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Creators/Authors contains: "Liu, Yanhong"

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  1. (, arXiv)
    In Integrating Reasoning Systems for Trustworthy AI, Proceedings of the 4th Workshop on Logic and Practice of Programming (LPOP 2024). https://arxiv.org/abs/2410.19738 
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    Free, publicly-accessible full text available October 1, 2026
  2. (, Springer)
    Invited talk abstract. In Proceedings of the 27th International Symposium on Practical Aspects of Declarative Languages (PADL 2025), https://doi.org/10.1007/978-3-031-84924-4 
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    Free, publicly-accessible full text available March 17, 2026
  3. ; (, Electronic Proceedings in Theoretical Computer Science)
    In Proceedings of the 40th International Conference on Logic Programming (ICLP 2024). 
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    Free, publicly-accessible full text available February 13, 2026
  4. ; (, Electronic Proceedings in Theoretical Computer Science)
    In Proceedings of the 40th International Conference on Logic Programming (ICLP 2024) 
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    Free, publicly-accessible full text available February 13, 2026
  5. ; (, ACM)
    Full Text Available 
  6. ; ; ; (, EPTCS, Open Publishing Association)
    Cabalar, Pedro; Fabiano, Francesco; Gebser, Martin; Gupta, Gopal; Swift, Theresa (Ed.)
    In Proceedings of the 40th International Conference on Logic Programming (ICLP 2024). https://dx.doi.org/10.4204/EPTCS.416 
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    Free, publicly-accessible full text available February 11, 2026
  7. ; (, arXiv)
    https://arxiv.org/abs/2410.19738 
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    Accepted Manuscript Full Text Available
  8. ; (, arXiv)
    https://arxiv.org/abs/2409.19211 
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    Accepted Manuscript Full Text Available
  9. ; (, IEEE)
    Full Text Available 
  10. (, ACM)
    Incremental computation aims to compute more efficiently on changed input by reusing previously computed results. We give a high-level overview of works on incremental computation, and highlight the essence underlying all of them, which we call incrementalization—the discrete counterpart of differentiation in calculus. We review the gist of a systematic method for incrementalization, and a systematic method centered around it, called Iterate-Incrementalize-Implement, for program design and optimization, as well as algorithm design and optimization. At a meta-level, with historical contexts and for future directions, we stress the power of high-level data, control, and module abstractions in developing new and better algorithms and programs as well as their precise complexities. 
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    Full Text Available