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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 @book{shalev2014understanding,
     11     title={Understanding Machine Learning: From Theory to Algorithms},
     12     author={Shalev-Shwartz, S. and Ben-David, S.},
     13     isbn={9781107057135},
     14     lccn={2014001779},
     15     series={Understanding Machine Learning: From Theory to Algorithms},
     16     url={https://books.google.pt/books?id=ttJkAwAAQBAJ},
     17     year={2014},
     18     publisher={Cambridge University Press}
     19 }
     20 
     21 @misc{li2018stochastic,
     22       title={Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations},
     23       author={Qianxiao Li and Cheng Tai and Weinan E},
     24       year={2018},
     25       eprint={1811.01558},
     26       archivePrefix={arXiv},
     27       primaryClass={cs.LG}
     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 }