# BORA: Bayesian Optimization for Resource Allocation

arXiv: https://arxiv.org/abs/2210.05977 · HTML: https://mlsanity.com/paper/2210.05977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Efficient Learning in Large-Scale Combinatorial Semi-Bandits](https://mlsanity.com/paper/1406.7443/) (2014)
- [Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation](https://mlsanity.com/paper/2006.04296/) (2020)
- [An efficient algorithm for learning with semi-bandit feedback](https://mlsanity.com/paper/1305.2732/) (2013)
- [Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback](https://mlsanity.com/paper/2101.08534/) (2021)
- [Combinatorial Semi-Bandit in the Non-Stationary Environment](https://mlsanity.com/paper/2002.03580/) (2020)
- [Semi-bandit Optimization in the Dispersed Setting](https://mlsanity.com/paper/1904.09014/) (2019)
- [Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback](https://mlsanity.com/paper/1909.01504/) (2019)
- [Optimal Resource Allocation with Semi-Bandit Feedback](https://mlsanity.com/paper/1406.3840/) (2014)
- [A Better Resource Allocation Algorithm with Semi-Bandit Feedback](https://mlsanity.com/paper/1803.10415/) (2018)
- [Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism](https://mlsanity.com/paper/2011.00330/) (2020)
- [Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget](https://mlsanity.com/paper/2202.04487/) (2022)
- [Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment](https://mlsanity.com/paper/2112.03024/) (2021)

## Abstract (arXiv)

Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit Feedback (SBF) is the reference method for efficiently solving this problem. In this paper we propose (i) an extension of the optimal resource allocation to a more general class of problems, specifically with resources availability changing over time, and (ii) Bayesian Optimization as a more efficient alternative to SBF. Three algorithms for Bayesian Optimization for Resource Allocation, namely BORA, are presented, working on allocation decisions represented as numerical vectors or distributions. The second option required to consider the Wasserstein distance as a more suitable metric to use into one of the BORA algorithms. Results on (i) the original SBF case study proposed in the literature, and (ii) a real-life application (i.e., the optimization of multi-channel marketing) empirically prove that BORA is a more efficient and effective learning-and-optimization framework than SBF.