# Distributed Stochastic Bandit Learning with Delayed Context Observation

arXiv: https://arxiv.org/abs/2207.14391 · HTML: https://mlsanity.com/paper/2207.14391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Conservative Contextual Linear Bandits](https://mlsanity.com/paper/1611.06426/) (2016)
- [Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits](https://mlsanity.com/paper/2003.01312/) (2020)
- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Stochastic Bandits with Context Distributions](https://mlsanity.com/paper/1906.02685/) (2019)
- [Distributed Bandit Online Convex Optimization with Time-Varying Coupled Inequality Constraints](https://mlsanity.com/paper/1912.03719/) (2019)
- [Stochastic Conservative Contextual Linear Bandits](https://mlsanity.com/paper/2203.15629/) (2022)

## Built on by (most cited first, 2 of 2)

- [Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints](https://mlsanity.com/paper/2401.11563/) (2024)
- [Federated Learning for Heterogeneous Bandits with Unobserved Contexts](https://mlsanity.com/paper/2303.17043/) (2023)

## Abstract (arXiv)

We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K>>M. The goal of the agents is to simultaneously minimize the cumulative regret over all the agents over a time horizon T. We consider a setting where the exact context is observed after a delay and at the time of choosing the action the agents are unaware of the context and only a distribution on the set of contexts is available. Such a situation arises in different applications where at the time of the decision the context needs to be predicted (e.g., weather forecasting or stock market prediction), and the context can be estimated once the reward is obtained. We propose an Upper Confidence Bound (UCB)-based distributed algorithm and prove the regret and communications bounds for linearly parametrized reward functions. We validated the performance of our algorithm via numerical simulations on synthetic data and real-world Movielens data.