Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem
<|reference_start|>Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem: This work continues to investigate the link between differentially private (DP) and online learning. Alon, Livni, Malliaris, and Moran (2019) showed that for binary concept classes, DP learnability of a given class implies that it has a finite Littlestone dimension (equivalently, that it is online learnable). Their proof relies on a model-theoretic result by Hodges (1997), which demonstrates that any binary concept class with a large Littlestone dimension contains a large subclass of thresholds. In a follow-up work, Jung, Kim, and Tewari (2020) extended this proof to multiclass PAC learning with a bounded number of labels. Unfortunately, Hodges's result does not apply in other natural settings such as multiclass PAC learning with an unbounded label space, and PAC learning of partial concept classes.   This naturally raises the question of whether DP learnability continues to imply online learnability in more general scenarios: indeed, Alon, Hanneke, Holzman, and Moran (2021) explicitly leave it as an open question in the context of partial concept classes, and the same question is open in the general multiclass setting. In this work, we give a positive answer to these questions showing that for general classification tasks, DP learnability implies online learnability. Our proof reasons directly about Littlestone trees, without relying on thresholds. We achieve this by establishing several Ramsey-type theorems for trees, which might be of independent interest.<|reference_end|>
@article{fioravanti2024ramsey,
    title={Ramsey Theorems for Trees and a General 'Private Learning Implies Online
  Learning' Theorem},
    author={Simone Fioravanti, Steve Hanneke, Shay Moran, Hilla Schefler, Iska
  Tsubari},
    journal={arXiv preprint arXiv:2407.07765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07765},
    primaryClass={cs.LG cs.CR cs.DS math.CO stat.ML}
}