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.
| Original language | English (US) |
|---|---|
| Article number | 104013 |
| Journal | Journal of Statistical Mechanics: Theory and Experiment |
| Volume | 2024 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 31 2024 |
Keywords
- deep learning
- learning theory
- machine learning
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Statistics, Probability and Uncertainty