Search Machine Learning Repository: @inproceedings{icml2014c2_shamir14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Communication-Efficient Distributed Optimization using an Approximate Newton-type Method},
    Url = {http://jmlr.org/proceedings/papers/v32/shamir14.pdf},
    Abstract = {We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably \emph{improves} with the data size, requiring an essentially constant number of iterations under reasonable assumptions. We provide theoretical and empirical evidence of the advantages of our method compared to other approaches, such as one-shot parameter averaging and ADMM.},
    Author = {Ohad Shamir and Nati Srebro and Tong Zhang},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1000-1008}
   }