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Award ID contains: 1910321

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  1. ; ; ; ( , The Eleventh International Conference on Learning Representation (ICLR))
    Free, publicly-accessible full text available May 1, 2024
  2. ; ; ; ( , Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  3. ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  4. ; ; ; ( , Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  5. ; ; ( , Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  6. ; ; ; ( , Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  7. ; ; ; ( , 28th International Conference on Principles and Practice of Constraint Programming (CP 2022))
    Accepted Manuscript Full Text Available
  8. ; ; ; ( , Proceedings of Thirty Fifth Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  9. ; ; ; ( , Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ( , 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