Online Learning and Disambiguations of Partial Concept Classes
Not on the map. Why?
Abstract (arXiv)
In a recent article, Alon, Hanneke, Holzman, and Moran (FOCS '21) introduced a unifying framework to study the learnability of classes of partial concepts. One of the central questions studied in their work is whether the learnability of a partial concept class is always inherited from the learnability of some ``extension'' of it to a total concept class. They showed this is not the case for PAC learning but left the problem open for the stronger notion of online learnability. We resolve this problem by constructing a class of partial concepts that is online learnable, but no extension of it to a class of total concepts is online learnable (or even PAC learnable).
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 1 of its 14 references that are in the map. First posted on arXiv 30 Mar 2023.
Builds on
1 of 14 references.
References · 1
- 2021A Theory of PAC Learnability of Partial Concept Classescited by 60
Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 1.
5 papers.
- 2024List Sample Compression and Uniform Convergencecited by 10
- 2024Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theoremcited by 6
- 2025Borsuk-Ulam and Replicable Learning of Large-Margin Halfspacescited by 4
- 2025Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learningcited by 2
- 2026Optimal Unambiguous DNFs and Alon-Saks-Seymourcited by 0
- 2026Optimal Unambiguous DNFs and Alon-Saks-Seymour3 Aug 2026
- 2025Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning30 May 2025
- 2025Borsuk-Ulam and Replicable Learning of Large-Margin Halfspaces19 Mar 2025
- 2024Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem10 Jul 2024
- 2024List Sample Compression and Uniform Convergence16 Mar 2024