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      1 @misc{andriushchenko2023sgd,
      2     title={SGD with Large Step Sizes Learns Sparse Features},
      3     author={Maksym Andriushchenko and Aditya Varre and Loucas Pillaud-Vivien and Nicolas Flammarion},
      4     year={2023},
      5     eprint={2210.05337},
      6     archivePrefix={arXiv},
      7     primaryClass={cs.LG}
      8 }
      9 
     10 @article{fast_armijo_2022,
     11     author = {Hafshejani, Sajad and Gaur, Daya and Hossain, Shahadat and Benkoczi, Robert},
     12     year = {2022},
     13     month = {11},
     14     pages = {},
     15     title = {Fast Armijo line search for stochastic gradient descent},
     16     doi = {10.21203/rs.3.rs-2285238/v1}
     17 }
     18 
     19 @book{shalev2014understanding,
     20    title={Understanding Machine Learning: From Theory to Algorithms},
     21    author={Shalev-Shwartz, S. and Ben-David, S.},
     22    isbn={9781107057135},
     23    lccn={2014001779},
     24    series={Understanding Machine Learning: From Theory to Algorithms},
     25    url={https://books.google.pt/books?id=ttJkAwAAQBAJ},
     26    year={2014},
     27    publisher={Cambridge University Press}
     28 }
     29 
     30 @misc{pillaudvivien2022label,
     31       title={Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation},
     32       author={Loucas Pillaud-Vivien and Julien Reygner and Nicolas Flammarion},
     33       year={2022},
     34       eprint={2206.09841},
     35       archivePrefix={arXiv},
     36       primaryClass={stat.ML}
     37 }
     38 
     39 
     40 @misc{li2018stochastic,
     41       title={Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations},
     42       author={Qianxiao Li and Cheng Tai and Weinan E},
     43       year={2018},
     44       eprint={1811.01558},
     45       archivePrefix={arXiv},
     46       primaryClass={cs.LG}
     47 }
     48