Learning theory
When and why a learner can generalize from a finite sample.
- Statistical learning framework
- PAC learning
- VC dimension and growth
- Rademacher and covering
- Generalization bounds
- PAC-Bayes
- Stability and generalization
- Online learning theory
- Learning in the limit
- Computational hardness of learning
- No free lunch and inductive bias
- Deep learning theory
- Kernel regime and NTK
- Implicit bias and overparameterization
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