# PyXAB -- A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms

arXiv: https://arxiv.org/abs/2303.04030 · HTML: https://mlsanity.com/paper/2303.04030/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Optimal rates for zero-order convex optimization: the power of two function evaluations](https://mlsanity.com/paper/1312.2139/) (2013)
- [An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback](https://mlsanity.com/paper/1507.08752/) (2015)
- [A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption](https://mlsanity.com/paper/1810.00997/) (2018)
- [Online Stochastic Optimization under Correlated Bandit Feedback](https://mlsanity.com/paper/1402.0562/) (2014)
- [Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization](https://mlsanity.com/paper/2106.09215/) (2021)
- [Federated X-Armed Bandit](https://mlsanity.com/paper/2205.15268/) (2022)

## Built on by (most cited first, 4 of 4)

- [Robust Lipschitz Bandits to Adversarial Corruptions](https://mlsanity.com/paper/2305.18543/) (2023)
- [Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions](https://mlsanity.com/paper/2402.15188/) (2024)
- [Personalized Federated X -armed Bandit](https://mlsanity.com/paper/2310.16323/) (2023)
- [Instance-dependent Stochastic Lipschitz bandit](https://mlsanity.com/paper/2605.29748/) (2026)

## Abstract (arXiv)

We introduce a Python open-source library for X-armed bandit and online blackbox optimization named PyXAB. PyXAB contains the implementations for more than 10 X-armed bandit algorithms, such as HOO, StoSOO, HCT, and the most recent works GPO and VHCT. PyXAB also provides the most commonly-used synthetic objectives to evaluate the performance of different algorithms and the various choices of the hierarchical partitions on the parameter space. The online documentation for PyXAB includes clear instructions for installation, straight-forward examples, detailed feature descriptions, and a complete reference of the API. PyXAB is released under the MIT license in order to encourage both academic and industrial usage. The library can be directly installed from PyPI with its source code available at https://github.com/WilliamLwj/PyXAB