Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent

Stéphan Clémençon, Patrice Bertail, Emilie Chautru. Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent. In Jimmy Lin, Jian Pei, Xiaohua Hu, Wo Chang, Raghunath Nambiar, Charu Aggarwal, Nick Cercone, Vasant Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne, editors, 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014. pages 25-30, IEEE, 2014. [doi]

@inproceedings{ClemenconBC14,
  title = {Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent},
  author = {Stéphan Clémençon and Patrice Bertail and Emilie Chautru},
  year = {2014},
  doi = {10.1109/BigData.2014.7004208},
  url = {http://dx.doi.org/10.1109/BigData.2014.7004208},
  researchr = {https://researchr.org/publication/ClemenconBC14},
  cites = {0},
  citedby = {0},
  pages = {25-30},
  booktitle = {2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014},
  editor = {Jimmy Lin and Jian Pei and Xiaohua Hu and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne},
  publisher = {IEEE},
  isbn = {978-1-4799-5665-4},
}