Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent

Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. Yuan, H., Lian, X., Li, C. J., Liu, J., & Hu, W. In Wallach, H. M., Larochelle, H., Beygelzimer, A., d'Alché-Buc , F., Fox, E. B., & Garnett, R., editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, pages 6926–6935, 2019.

Paper bibtex

@inproceedings{DBLP:conf/nips/YuanLLLH19,
  author    = {Huizhuo Yuan and
               Xiangru Lian and
               Chris Junchi Li and
               Ji Liu and
               Wenqing Hu},
  editor    = {Hanna M. Wallach and
               Hugo Larochelle and
               Alina Beygelzimer and
               Florence d'Alch{\'{e}}{-}Buc and
               Emily B. Fox and
               Roman Garnett},
  title     = {Efficient Smooth Non-Convex Stochastic Compositional Optimization
               via Stochastic Recursive Gradient Descent},
  booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
               on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14
               December 2019, Vancouver, BC, Canada},
  pages     = {6926--6935},
  year      = {2019},
  url       = {http://papers.nips.cc/paper/8916-efficient-smooth-non-convex-stochastic-compositional-optimization-via-stochastic-recursive-gradient-descent},
  timestamp = {Fri, 06 Mar 2020 16:59:09 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/YuanLLLH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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