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Creators/Authors contains: "DeBlasio, D."

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  1. ; ; ; ; ; (, STOC Annual Conference)
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, STOC annual conference)
    Full Text Available 
  3. ; ; ; ; ; (, STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing)
    Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case guarantee. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that data-driven algorithm design can lead to significant improvements in performance. This approach uses a training set of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; (, not applicable - unpublished manuscript)
    Accepted Manuscript Full Text Available