# Regret Analysis of Sleeping Competing Bandits

arXiv: https://arxiv.org/abs/2603.19700 · HTML: https://mlsanity.com/paper/2603.19700/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Explore First, Exploit Next: The True Shape of Regret in Bandit Problems](https://mlsanity.com/paper/1602.07182/) (2016)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [Blocking Bandits](https://mlsanity.com/paper/1907.11975/) (2019)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Matching while Learning](https://mlsanity.com/paper/1603.04549/) (2016)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Player-optimal Stable Regret for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2307.10890/) (2023)
- [Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets](https://mlsanity.com/paper/2206.02344/) (2022)
- [Thompson Sampling for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2204.12048/) (2022)

## Built on by (most cited first, 1 of 1)

- [Exact Regret Frontiers and Externality Scheduling in Centralized Serial-Dictatorship Bandits](https://mlsanity.com/paper/2609.19963/) (2026)

## Abstract (arXiv)

The Competing Bandits framework is a recently emerging area that integrates multi-armed bandits in online learning with stable matching in game theory. While conventional models assume that all players and arms are constantly available, in real-world problems, their availability can vary arbitrarily over time. In this paper, we formulate this setting as Sleeping Competing Bandits. To analyze this problem, we naturally extend the regret definition used in existing competing bandits and derive regret bounds for the proposed model. We propose an algorithm that simultaneously achieves an asymptotic regret bound of Oleft(NKlog Ti/Delta2right) under reasonable assumptions, where N is the number of players, K is the number of arms, Ti is the number of rounds of each player pi, and Delta is the minimum reward gap. We also provide a regret lower bound of Omegaleft( N(K-N+1)log Ti/Delta2 right) under the same assumptions. This implies that our algorithm is asymptotically optimal in the regime where the number of arms K is relatively larger than the number of players N.