# Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

arXiv: https://arxiv.org/abs/2010.15390 · HTML: https://mlsanity.com/paper/2010.15390/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Hierarchical Bayesian Bandits](https://mlsanity.com/paper/2111.06929/) (2021)
- [Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models](https://mlsanity.com/paper/2108.06422/) (2021)
- [Provably Efficient Multi-Task Reinforcement Learning with Model Transfer](https://mlsanity.com/paper/2107.08622/) (2021)
- [Online Clustering of Bandits with Misspecified User Models](https://mlsanity.com/paper/2310.02717/) (2023)
- [Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards](https://mlsanity.com/paper/2306.05579/) (2023)
- [Best Arm Identification with Possibly Biased Offline Data](https://mlsanity.com/paper/2505.23165/) (2025)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [Thompson Sampling for Robust Transfer in Multi-Task Bandits](https://mlsanity.com/paper/2206.08556/) (2022)
- [Transfer in Sequential Multi-armed Bandits via Reward Samples](https://mlsanity.com/paper/2403.12428/) (2024)
- [Regret Lower Bounds in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2308.08046/) (2023)
- [Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2402.04417/) (2024)
- [Finite-Time Logarithmic Bayes Regret Upper Bounds](https://mlsanity.com/paper/2306.09136/) (2023)
- [Locally Private Nonparametric Contextual Multi-armed Bandits](https://mlsanity.com/paper/2503.08098/) (2025)
- [Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits](https://mlsanity.com/paper/2512.12693/) (2025)

## Abstract (arXiv)

In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this problem as the ε-multi-player multi-armed bandit problem, in which a set of players concurrently interact with a set of arms, and for each arm, the reward distributions for all players are similar but not necessarily identical. We develop an upper confidence bound-based algorithm, RobustAgg(ε), that adaptively aggregates rewards collected by different players. In the setting where an upper bound on the pairwise similarities of reward distributions between players is known, we achieve instance-dependent regret guarantees that depend on the amenability of information sharing across players. We complement these upper bounds with nearly matching lower bounds. In the setting where pairwise similarities are unknown, we provide a lower bound, as well as an algorithm that trades off minimax regret guarantees for adaptivity to unknown similarity structure.