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High-arity PAC learning via exchangeability
We develop a theory of high-arity PAC learning, which is statistical learning in the presence of "structured correlation".
The unstable formula theorem revisited via algorithms
This paper is about the surprising interaction of a foundational result from model theory about stability of theories, which seems to be inherently about the infinite, with algorithmic stability in learning.
Agnostic Online Learning and Excellent Sets
We use algorithmic methods from online learning to revisit a key idea from the interaction of model theory and combinatorics, the existence of large "indivisible" sets, called "$\epsilon$-excellent," in $k$-edge stable graphs (equivalently, Littlestone classes).
Private PAC learning implies finite Littlestone dimension
We show that every approximately differentially private learning algorithm (possibly improper) for a class $H$ with Littlestone dimension~$d$ requires $\Omega\bigl(\log^*(d)\bigr)$ examples.
