Kernels, Data & Physics

Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam, Nikolaos Tsilivis, Julia Kempe
7/5/2023
cs.LGstat.ML

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Abstract

Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.

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Cite this paper

@article{cagnetta2023kernels,
  title  = {Kernels, Data & Physics},
  author = {Francesco Cagnetta and Deborah Oliveira and Mahalakshmi Sabanayagam and Nikolaos Tsilivis and Julia Kempe},
  year   = {2023},
  eprint = {2307.02693},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.02693v1}
}

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