mlsanityData to 14 Oct 2026

Bandit Convex Optimisation

arXiv 2402.06535 ↗First posted 9 Feb 2024math.OC cs.LG stat.MLcited by 19

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 8.

19 papers.

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  1. 2024Online Newton Method for Bandit Convex Optimisationcited by 12
  2. 2024Improved Regret for Bandit Convex Optimization with Delayed Feedbackcited by 8
  3. 2024No-Regret Learning for Fair Multi-Agent Social Welfare Optimizationcited by 8
  4. 2026Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variationscited by 5
  5. 2026A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guaranteescited by 3
  6. 2024Tight Rates for Bandit Control Beyond Quadraticscited by 2
  7. 2024No-Regret Mnatural-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Settingcited by 1
  8. 2024A simple and improved algorithm for noisy, convex, zeroth-order optimisationcited by 1
  9. 2025Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimizationcited by 1
  10. 2025Non-stationary Bandit Convex Optimization: A Comprehensive Studycited by 1
  11. 2026Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learningcited by 1
  12. 2026Bandit Convex Optimization with Gradient Prediction Adaptivitycited by 1
7 more
  1. 2026Capacity-Constrained Online Convex Optimization with Delayed Feedbackcited by 1
  2. 2024A conversion theorem and minimax optimality for continuum contextual banditscited by 0
  3. 2025High-probability zeroth-order online convex optimisation beyond Euclidean geometrycited by 0
  4. 2025Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communicationscited by 0
  5. 2026A Perturbation Approach to Unconstrained Linear Banditscited by 0
  6. 2026Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedbackcited by 0
  7. 2026Nonlinear Banditcited by 0