On Learnability, Complexity and Stability
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Abstract (arXiv)
We consider the fundamental question of learnability of a hypotheses class in the supervised learning setting and in the general learning setting introduced by Vladimir Vapnik. We survey classic results characterizing learnability in term of suitable notions of complexity, as well as more recent results that establish the connection between learnability and stability of a learning algorithm.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2013.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stabilitycited by 29
- 2023Limits for Learning with Language Modelscited by 17
- 2024Re-examining learning linear functions in contextcited by 5
- 2026Stochastic Gradient Descent with Momentum is Algorithmically Stablecited by 0
- 2026Stochastic Gradient Descent with Momentum is Algorithmically Stable27 May 2026
- 2024Re-examining learning linear functions in context18 Nov 2024
- 2023Limits for Learning with Language Models21 Jun 2023
- 2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability12 Feb 2021