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Creators/Authors contains: "Zakynthinou, Lydia"

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  1. ; ; ; ( , Conference on Learning Theory)
  2. ; ; ( , International Conference on Machine Learning)
  3. ; ; ; ; ( , Advances in Neural Information Processing Systems 34 (NeurIPS 2021))
    Accepted Manuscript Full Text Available
  4. ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, decomposable submodular function under cardinality constraints is known as the Combinatorial Public Projects (CPP) problem (Papadimitriou, Schapira, and Singer 2008). Previous work by Gupta et al. (2010) gave a differentially private algorithm for the CPP problem. We extend this work by designing differentially private algorithms for both monotone and non-monotone decomposable submodular maximization under general matroid constraints, with competitive utility guarantees. We complement our theoretical bounds with experiments demonstrating improved empirical performance. 
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    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. ; ; ( , Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ( , Advances in Neural Information Processing Systems)
    null (Ed.)
    Accepted Manuscript Full Text Available