Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification

Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová. Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020. [doi]

@inproceedings{MignaccoKUZ20,
  title = {Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification},
  author = {Francesca Mignacco and Florent Krzakala and Pierfrancesco Urbani and Lenka Zdeborová},
  year = {2020},
  url = {https://proceedings.neurips.cc/paper/2020/hash/6c81c83c4bd0b58850495f603ab45a93-Abstract.html},
  researchr = {https://researchr.org/publication/MignaccoKUZ20},
  cites = {0},
  citedby = {0},
  booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual},
  editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria-Florina Balcan and Hsuan-Tien Lin},
}