# Best Arm Identification under Additive Transfer Bandits

arXiv: https://arxiv.org/abs/2112.04083 · HTML: https://mlsanity.com/paper/2112.04083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [Sim-to-Real Robot Learning from Pixels with Progressive Nets](https://mlsanity.com/paper/1610.04286/) (2016)
- [Sequential Experimental Design for Transductive Linear Bandits](https://mlsanity.com/paper/1906.08399/) (2019)
- [An optimal algorithm for the Thresholding Bandit Problem](https://mlsanity.com/paper/1605.08671/) (2016)
- [An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits](https://mlsanity.com/paper/2006.11685/) (2020)
- [The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime](https://mlsanity.com/paper/1702.05186/) (2017)
- [Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration](https://mlsanity.com/paper/1706.01081/) (2017)
- [Best Arm Identification in Generalized Linear Bandits](https://mlsanity.com/paper/1905.08224/) (2019)
- [Bounded Regret for Finite-Armed Structured Bandits](https://mlsanity.com/paper/1411.2919/) (2014)
- [Pure Exploration of Multi-armed Bandit Under Matroid Constraints](https://mlsanity.com/paper/1605.07162/) (2016)
- [Monte-Carlo Tree Search by Best Arm Identification](https://mlsanity.com/paper/1706.02986/) (2017)
- [Sample complexity of partition identification using multi-armed bandits](https://mlsanity.com/paper/1811.05654/) (2018)
- [Structured Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1706.05198/) (2017)
- [Maximin Action Identification: A New Bandit Framework for Games](https://mlsanity.com/paper/1602.04676/) (2016)
- [A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting](https://mlsanity.com/paper/1810.08164/) (2018)

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

- [Max-Quantile Grouped Infinite-Arm Bandits](https://mlsanity.com/paper/2210.01295/) (2022)

## Abstract (arXiv)

We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms. In this paper, we study the setting when, despite the means being unknown, there is a known additive relationship between the source and target MAB instances. We show how our framework covers a range of previously studied pure exploration problems and additionally captures new problems. We propose and theoretically analyze an LUCB-style algorithm to identify an ε-optimal target arm with high probability. Our theoretical analysis highlights aspects of this transfer learning problem that do not arise in the typical BAI setup, and yet recover the LUCB algorithm for single domain BAI as a special case.