The Galerkin method beats Graph-Based Approaches for Spectral Algorithms

@InProceedings{pmlr-v238-cabannes24a,
  title = 	 {The {G}alerkin method beats Graph-Based Approaches for Spectral Algorithms},
  author =       {Cabannes, Vivien A. and Bach, Francis},
  booktitle = 	 {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {451--459},
  year = 	 {2024},
  editor = 	 {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
  volume = 	 {238},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {02--04 May},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v238/cabannes24a/cabannes24a.pdf},
  url = 	 {https://proceedings.mlr.press/v238/cabannes24a.html},
  abstract = 	 {Historically, the machine learning community has derived spectral decompositions from graph-based approaches. We break with this approach and prove the statistical and computational superiority of the Galerkin method, which consists in restricting the study to a small set of test functions. In particular, we introduce implementation tricks to deal with differential operators in large dimensions with structured kernels. Finally, we extend on the core principles beyond our approach to apply them to non-linear spaces of functions, such as the ones parameterized by deep neural networks, through loss-based optimization procedures.}
}