Empirical Inference Conference Paper 2025

Avoiding spurious sharpness minimization broadens applicability of SAM

arXiv
Thumb ticker sm 20211026 sidak pal singh wolfram scheible short
Empirical Inference
Collaborator

Author(s): Singh, S. P. and Mobahi, H. and Agarwala, A. and Dauphin, Y.
Links:
Book Title: Proceedings of the 42nd International Conference on Machine Learning (ICML)
Volume: 267
Pages: 55702--55719
Year: 2025
Month: July
Series: Proceedings of Machine Learning Research
Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry
Publisher: PMLR
BibTeX Type: Conference Paper (conference)
Event Name: International Conference on Machine Learning
Event Place: Vancouver Convention Center
State: Published
URL: https://proceedings.mlr.press/v267/singh25b.html

BibTeX

@conference{SinMobAgaDau25,
  title = {Avoiding spurious sharpness minimization broadens applicability of SAM},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
  volume = {267},
  pages = {55702--55719},
  series = {Proceedings of Machine Learning Research},
  editors = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry},
  publisher = {PMLR},
  month = jul,
  year = {2025},
  author = {Singh, S. P. and Mobahi, H. and Agarwala, A. and Dauphin, Y.},
  url = {https://proceedings.mlr.press/v267/singh25b.html},
  month_numeric = {7}
}