# Gaussian Process Bandits with Aggregated Feedback

arXiv: https://arxiv.org/abs/2112.13029 · HTML: https://mlsanity.com/paper/2112.13029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Learning from Aggregate Observations](https://mlsanity.com/paper/2004.06316/) (2020)
- [Top-k Combinatorial Bandits with Full-Bandit Feedback](https://mlsanity.com/paper/1905.12624/) (2019)
- [Gaussian Process bandits with adaptive discretization](https://mlsanity.com/paper/1712.01447/) (2017)
- [Quantile Bandits for Best Arms Identification](https://mlsanity.com/paper/2010.11568/) (2020)

## Built on by (most cited first, 3 of 3)

- [Dynamical Linear Bandits](https://mlsanity.com/paper/2211.08997/) (2022)
- [Stochastic Contextual Bandits with Long Horizon Rewards](https://mlsanity.com/paper/2302.00814/) (2023)
- [Indirect Query Bayesian Optimization with Integrated Feedback](https://mlsanity.com/paper/2412.13559/) (2024)

## Abstract (arXiv)

We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. This is motivated by applications where the precise rewards are impossible or expensive to obtain, while an aggregated reward or feedback, such as the average over a subset, is available. We constrain the set of reward functions by assuming that they are from a Gaussian Process and propose the Gaussian Process Optimistic Optimisation (GPOO) algorithm. We adaptively construct a tree with nodes as subsets of the arm space, where the feedback is the aggregated reward of representatives of a node. We propose a new simple regret notion with respect to aggregated feedback on the recommended arms. We provide theoretical analysis for the proposed algorithm, and recover single point feedback as a special case. We illustrate GPOO and compare it with related algorithms on simulated data.