# Fast Information-theoretic Bayesian Optimisation

arXiv: https://arxiv.org/abs/1711.00673 · HTML: https://mlsanity.com/paper/1711.00673/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

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

- [Joint Entropy Search for Maximally-Informed Bayesian Optimization](https://mlsanity.com/paper/2206.04771/) (2022)
- [GIBBON: General-purpose Information-Based Bayesian OptimisatioN](https://mlsanity.com/paper/2102.03324/) (2021)
- [Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation](https://mlsanity.com/paper/2301.10123/) (2023)
- [Self-Correcting Bayesian Optimization through Bayesian Active Learning](https://mlsanity.com/paper/2304.11005/) (2023)
- [Model Predictive Task Sampling for Efficient and Robust Adaptation](https://mlsanity.com/paper/2501.11039/) (2025)
- [Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/2107.14465/) (2021)
- [SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces](https://mlsanity.com/paper/2301.11832/) (2023)
- [Rectified Max-Value Entropy Search for Bayesian Optimization](https://mlsanity.com/paper/2202.13597/) (2022)
- [How Bayesian Should Bayesian Optimisation Be?](https://mlsanity.com/paper/2105.00894/) (2021)
- [MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation](https://mlsanity.com/paper/2203.16912/) (2022)
- [Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation](https://mlsanity.com/paper/2206.02437/) (2022)
- [Marginalising over Stationary Kernels with Bayesian Quadrature](https://mlsanity.com/paper/2106.07452/) (2021)
- [Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds](https://mlsanity.com/paper/2110.12087/) (2021)
- [Information-theoretic Bayesian Optimization: Survey and Tutorial](https://mlsanity.com/paper/2502.06789/) (2025)
- [Alpha Entropy Search for New Information-based Bayesian Optimization](https://mlsanity.com/paper/2411.16586/) (2024)
- [Quality and Computation Time in Optimization Problems](https://mlsanity.com/paper/2111.10595/) (2021)
- [A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization](https://mlsanity.com/paper/2402.04885/) (2024)
- [Respecting the limit:Bayesian optimization with a bound on the optimal value](https://mlsanity.com/paper/2411.04744/) (2024)
- [Towards real-time surrogate-free Bayesian inversion for neutron reflectometry](https://mlsanity.com/paper/2509.06924/) (2025)

## Abstract (arXiv)

Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choice of kernels available to model the objective. We develop a fast information-theoretic Bayesian Optimisation method, FITBO, that avoids the need for sampling the global minimiser, thus significantly reducing computational overhead. Moreover, in comparison with existing approaches, our method faces fewer constraints on kernel choice and enjoys the merits of dealing with the output space. We demonstrate empirically that FITBO inherits the performance associated with information-theoretic Bayesian optimisation, while being even faster than simpler Bayesian optimisation approaches, such as Expected Improvement.