mlsanityData to 7 Oct 2026

Simple online learning with consistent oracle

arXiv 2308.08055 ↗First posted 15 Aug 2023cs.LG cs.AI stat.MLcited by 5

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Abstract (arXiv)

We consider online learning in the model where a learning algorithm can access the class only via the consistent oracle -- an oracle, that, at any moment, can give a function from the class that agrees with all examples seen so far. This model was recently considered by Assos et al.~(COLT'23). It is motivated by the fact that standard methods of online learning rely on computing the Littlestone dimension of subclasses, a computationally intractable problem. Assos et al.~gave an online learning algorithm in this model that makes at most Cd mistakes on classes of Littlestone dimension d, for some absolute unspecified constant C > 0. We give a novel algorithm that makes at most O(256d) mistakes. Our proof is significantly simpler and uses only very basic properties of the Littlestone dimension. We also show that there exists no algorithm in this model that makes less than 3d mistakes.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

9 of 17 references.

References · 9

  1. 2020A Theory of Universal Learningcited by 59
  2. 2022Reproducibility in Learningcited by 44
  3. 2023Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalizationcited by 42
  4. 2023Statistical Indistinguishability of Learning Algorithmscited by 17
  5. 2023On Computable Online Learningcited by 12
  6. 2023Online Learning and Solving Infinite Games with an ERM Oraclecited by 11
  7. 2019Bounds in Query Learningcited by 8
  8. 2022Finite Littlestone Dimension Implies Finite Information Complexitycited by 7
  9. 2022Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Bicliquecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.