The Multiplicative Version of Azuma's Inequality, with an Application to Contention Analysis
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Abstract (arXiv)
Azuma's inequality is a tool for proving concentration bounds on random variables. The inequality can be thought of as a natural generalization of additive Chernoff bounds. On the other hand, the analogous generalization of multiplicative Chernoff bounds does not appear to be widely known. We formulate a multiplicative-error version of Azuma's inequality. We then show how to apply this new inequality in order to greatly simplify (and correct) the analysis of contention delays in multithreaded systems managed by randomized work stealing.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2021.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1.
5 papers.
- 2023Nash Regret Guarantees for Linear Banditscited by 9
- 2024Is Transductive Learning Equivalent to PAC Learning?cited by 4
- 2024On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UXcited by 0
- 2024Validity and efficiency of the conformal CUSUM procedurecited by 0
- 2025No-regret incentive-compatible online learning under exact truthfulness with non-myopic expertscited by 0
- 2025No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts17 Feb 2025
- 2024Validity and efficiency of the conformal CUSUM procedure4 Dec 2024
- 2024Is Transductive Learning Equivalent to PAC Learning?8 May 2024
- 2024On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX8 Apr 2024
- 2023Nash Regret Guarantees for Linear Bandits3 Oct 2023