mlsanityData to 14 Oct 2026

Bayesian Analysis of Combinatorial Gaussian Process Bandits

arXiv 2312.12676 ↗First posted 20 Dec 2023cs.LG cs.AIcited by 2

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Abstract (arXiv)

We consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to maximize the long-term cumulative reward. We study the Bayesian setting and provide novel Bayesian cumulative regret bounds for three GP-based algorithms: GP-UCB, GP-BayesUCB and GP-TS. Our bounds extend previous results for GP-UCB and GP-TS to the infinite, volatile and combinatorial setting, and to the best of our knowledge, we provide the first regret bound for GP-BayesUCB. Volatile arms encompass other widely considered bandit problems such as contextual bandits. Furthermore, we employ our framework to address the challenging real-world problem of online energy-efficient navigation, where we demonstrate its effectiveness compared to the alternatives.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 20 Dec 2023.

Builds on

13 of 46 references.

References · 13

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  2. 2013Gaussian Processes for Big Datacited by 383
  3. 2017On Kernelized Multi-armed Banditscited by 352
  4. 2013Learning to Optimize Via Posterior Samplingcited by 256
  5. 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Banditscited by 201
  6. 2020On Information Gain and Regret Bounds in Gaussian Process Banditscited by 158
  7. 2018Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Modelscited by 38
  8. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
  9. 2023Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Boundscited by 16
  10. 2021Online Learning of Energy Consumption for Navigation of Electric Vehiclescited by 7
  11. 2021Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budgetcited by 5
  12. 2021Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processescited by 2
1 more
  1. 2021Contextual Combinatorial Multi-output GP Bandits with Group Constraintscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Adaptive Prior Selection in Gaussian Process Bandits with Thompson Samplingcited by 2
  2. 2026Learning to Attack: A Bandit Approach to Adversarial Context Poisoningcited by 0