Kernels, Data & Physics

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.
Comment: These are notes from the lecture of Julia Kempe given at the summer school "Statistical Physics \& Machine Learning", that took place in Les Houches School of Physics in France from 4th to 29th July 2022