# Bandit Convex Optimisation

arXiv: https://arxiv.org/abs/2402.06535 · HTML: https://mlsanity.com/paper/2402.06535/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2024.

## Built on by (most cited first, 19 of 19)

- [Online Newton Method for Bandit Convex Optimisation](https://mlsanity.com/paper/2406.06506/) (2024)
- [Improved Regret for Bandit Convex Optimization with Delayed Feedback](https://mlsanity.com/paper/2402.09152/) (2024)
- [No-Regret Learning for Fair Multi-Agent Social Welfare Optimization](https://mlsanity.com/paper/2405.20678/) (2024)
- [Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations](https://mlsanity.com/paper/2602.04761/) (2026)
- [A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees](https://mlsanity.com/paper/2602.02634/) (2026)
- [Tight Rates for Bandit Control Beyond Quadratics](https://mlsanity.com/paper/2410.00993/) (2024)
- [No-Regret Mnatural-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting](https://mlsanity.com/paper/2405.12439/) (2024)
- [A simple and improved algorithm for noisy, convex, zeroth-order optimisation](https://mlsanity.com/paper/2406.18672/) (2024)
- [Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization](https://mlsanity.com/paper/2503.10836/) (2025)
- [Non-stationary Bandit Convex Optimization: A Comprehensive Study](https://mlsanity.com/paper/2506.02980/) (2025)
- [Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning](https://mlsanity.com/paper/2605.15411/) (2026)
- [Bandit Convex Optimization with Gradient Prediction Adaptivity](https://mlsanity.com/paper/2605.22191/) (2026)
- [Capacity-Constrained Online Convex Optimization with Delayed Feedback](https://mlsanity.com/paper/2606.11711/) (2026)
- [A conversion theorem and minimax optimality for continuum contextual bandits](https://mlsanity.com/paper/2406.05714/) (2024)
- [High-probability zeroth-order online convex optimisation beyond Euclidean geometry](https://mlsanity.com/paper/2509.21484/) (2025)
- [Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications](https://mlsanity.com/paper/2510.18299/) (2025)
- [A Perturbation Approach to Unconstrained Linear Bandits](https://mlsanity.com/paper/2603.28201/) (2026)
- [Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback](https://mlsanity.com/paper/2605.26373/) (2026)
- [Nonlinear Bandit](https://mlsanity.com/paper/2607.07304/) (2026)

## Abstract (arXiv)

Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. This book covers the many tools used for this problem, including cutting plane methods, interior point methods, continuous exponential weights, gradient descent and online Newton step. The nuances between the many assumptions and setups are explained. Although there is not much truly new here, some existing tools are applied in novel ways to obtain new algorithms. A few bounds are improved in minor ways.