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Creators/Authors contains: "Lattanzi, Silvio"

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  1. ; ; ; ; (, Foundations and Trends® in Optimization)
    Full Text Available 
  2. ; ; ; (, Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
    Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.F.; Lin, H. (Ed.)
    In this paper, we provide an efficient approximation algorithm for finding the most likelihood configuration (MAP) of size k for Determinantal Point Processes (DPP) in the online setting where the data points arrive in an arbitrary order and the algorithm cannot discard the selected elements from its local memory. Given a tolerance additive error eta, our online algorithm achieves a k^O(k) multiplicative approximation guarantee along with an additive error eta, using a memory footprint independent of the size of the data stream. We note that the exponential dependence on k in the approximation factor is unavoidable even in the offline setting. Our result readily implies a streaming algorithm with an improved memory bound compared to existing results. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Machine Learning and Knowledge Discovery in Databases)
    null (Ed.)
    Accepted Manuscript Full Text Available