# Bias-Robust Bayesian Optimization via Dueling Bandits

arXiv: https://arxiv.org/abs/2105.11802 · HTML: https://mlsanity.com/paper/2105.11802/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 21 of 21)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [High Dimensional Bayesian Optimisation and Bandits via Additive Models](https://mlsanity.com/paper/1503.01673/) (2015)
- [Stochastic bandits robust to adversarial corruptions](https://mlsanity.com/paper/1803.09353/) (2018)
- [Preference-based Online Learning with Dueling Bandits: A Survey](https://mlsanity.com/paper/1807.11398/) (2018)
- [Better Algorithms for Stochastic Bandits with Adversarial Corruptions](https://mlsanity.com/paper/1902.08647/) (2019)
- [Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces](https://mlsanity.com/paper/1902.03229/) (2019)
- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [Adversarially Robust Optimization with Gaussian Processes](https://mlsanity.com/paper/1810.10775/) (2018)
- [Distributionally Robust Bayesian Optimization](https://mlsanity.com/paper/2002.09038/) (2020)
- [Information Directed Sampling for Linear Partial Monitoring](https://mlsanity.com/paper/2002.11182/) (2020)
- [Multi-dueling Bandits with Dependent Arms](https://mlsanity.com/paper/1705.00253/) (2017)
- [Corruption-Tolerant Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2003.01971/) (2020)
- [Asymptotically Optimal Information-Directed Sampling](https://mlsanity.com/paper/2011.05944/) (2020)
- [Stochastic Linear Optimization with Adversarial Corruption](https://mlsanity.com/paper/1909.02109/) (2019)
- [Semiparametric Contextual Bandits](https://mlsanity.com/paper/1803.04204/) (2018)
- [Zeroth Order Non-convex optimization with Dueling-Choice Bandits](https://mlsanity.com/paper/1911.00980/) (2019)
- [Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model](https://mlsanity.com/paper/1901.11221/) (2019)
- [Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces](https://mlsanity.com/paper/1707.02375/) (2017)
- [Regret Minimization in Stochastic Contextual Dueling Bandits](https://mlsanity.com/paper/2002.08583/) (2020)

## Built on by (most cited first, 11 of 11)

- [Data-Driven Offline Decision-Making via Invariant Representation Learning](https://mlsanity.com/paper/2211.11349/) (2022)
- [Multi-Fidelity Bayesian Optimization with Unreliable Information Sources](https://mlsanity.com/paper/2210.13937/) (2022)
- [Optimal Design for Human Preference Elicitation](https://mlsanity.com/paper/2404.13895/) (2024)
- [Robust and Conjugate Gaussian Process Regression](https://mlsanity.com/paper/2311.00463/) (2023)
- [Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications](https://mlsanity.com/paper/2302.03683/) (2023)
- [A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits](https://mlsanity.com/paper/2202.01850/) (2022)
- [Bandits with Preference Feedback: A Stackelberg Game Perspective](https://mlsanity.com/paper/2406.16745/) (2024)
- [Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.04376/) (2023)
- [Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget](https://mlsanity.com/paper/2202.04487/) (2022)
- [Regret Minimization via Saddle Point Optimization](https://mlsanity.com/paper/2403.10379/) (2024)
- [Robust Bayesian Optimisation with Unbounded Corruptions](https://mlsanity.com/paper/2511.15315/) (2025)

## Abstract (arXiv)

We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reduction of the confounded setting to the dueling bandit model. Then we propose a novel approach for dueling bandits based on information-directed sampling (IDS). Thereby, we obtain the first efficient kernelized algorithm for dueling bandits that comes with cumulative regret guarantees. Our analysis further generalizes a previously proposed semi-parametric linear bandit model to non-linear reward functions, and uncovers interesting links to doubly-robust estimation.