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Effective Littlestone Dimension

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abstract

Delle Rose et al.~(COLT'23) introduced an effective version of the Vapnik-Chervonenkis dimension, and showed that it characterizes improper PAC learning with total computable learners. In this paper, we introduce and study a similar effectivization of the notion of Littlestone dimension. Finite effective Littlestone dimension is a necessary condition for computable online learning but is not a sufficient one -- which we already establish for classes of the effective Littlestone dimension 2. However, the effective Littlestone dimension equals the optimal mistake bound for computable learners in two special cases: a) for classes of Littlestone dimension 1 and b) when the learner receives as additional information an upper bound on the numbers to be guessed. Interestingly, finite effective Littlestone dimension also guarantees that the class consists only of computable functions.

fields

cs.LG 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

On the Computability of Multiclass PAC Learning

cs.LG · 2025-02-10 · conditional · novelty 7.0

Computable multiclass PAC learnability for finite label spaces is characterized exactly by the finiteness of computable distinguisher dimensions, including a new computable Natarajan dimension.

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  • On the Computability of Multiclass PAC Learning cs.LG · 2025-02-10 · conditional · none · ref 17 · internal anchor

    Computable multiclass PAC learnability for finite label spaces is characterized exactly by the finiteness of computable distinguisher dimensions, including a new computable Natarajan dimension.