# Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application

arXiv: https://arxiv.org/abs/2307.14549 · HTML: https://mlsanity.com/paper/2307.14549/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays](https://mlsanity.com/paper/1506.00779/) (2015)
- [Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards](https://mlsanity.com/paper/2004.06248/) (2020)
- [Hardness of Online Sleeping Combinatorial Optimization Problems](https://mlsanity.com/paper/1509.03600/) (2015)

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

- [Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing](https://mlsanity.com/paper/2512.21626/) (2025)

## Abstract (arXiv)

This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The problem involves bounded, adversarial loss and unknown i.i.d. distributions for arm availability. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to achieve theoretical performance with regret upper bounded by bigO(kN2√Tlog T), where k is the number of arms selected per time step, N is the total number of arms, and T is the time horizon.