# Bayesian Optimization with Exponential Convergence

arXiv: https://arxiv.org/abs/1604.01348 · HTML: https://mlsanity.com/paper/1604.01348/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 18 of 18)

- [Optimal Order Simple Regret for Gaussian Process Bandits](https://mlsanity.com/paper/2108.09262/) (2021)
- [Pareto Set Learning for Expensive Multi-Objective Optimization](https://mlsanity.com/paper/2210.08495/) (2022)
- [The Behavior and Convergence of Local Bayesian Optimization](https://mlsanity.com/paper/2305.15572/) (2023)
- [Derivative-Free Reinforcement Learning: A Review](https://mlsanity.com/paper/2102.05710/) (2021)
- [Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization](https://mlsanity.com/paper/2506.01393/) (2025)
- [Understanding Dynamics of Nonlinear Representation Learning and Its Application](https://mlsanity.com/paper/2106.14836/) (2021)
- [Fair Clustering Using Antidote Data](https://mlsanity.com/paper/2106.00600/) (2021)
- [Multi-objective Optimization by Learning Space Partitions](https://mlsanity.com/paper/2110.03173/) (2021)
- [Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization](https://mlsanity.com/paper/2106.08598/) (2021)
- [Optimization on Manifolds via Graph Gaussian Processes](https://mlsanity.com/paper/2210.10962/) (2022)
- [Bayesian Optimistic Optimisation with Exponentially Decaying Regret](https://mlsanity.com/paper/2105.04332/) (2021)
- [Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization](https://mlsanity.com/paper/2311.09591/) (2023)
- [A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection](https://mlsanity.com/paper/2412.13913/) (2024)
- [Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?](https://mlsanity.com/paper/2503.03625/) (2025)
- [OneAdapt: Fast Configuration Adaptation for Video Analytics Applications via Backpropagation](https://mlsanity.com/paper/2310.02422/) (2023)
- [LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions](https://mlsanity.com/paper/2311.11328/) (2023)
- [Power of human-algorithm collaboration in solving combinatorial optimization problems](https://mlsanity.com/paper/2107.11784/) (2021)
- [Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization](https://mlsanity.com/paper/2509.17405/) (2025)

## Abstract (arXiv)

This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence requires access to the delta-cover sampling, which was considered to be impractical. Our approach eliminates both requirements and achieves an exponential convergence rate.