# High Dimensional Bayesian Optimization Using Dropout

arXiv: https://arxiv.org/abs/1802.05400 · HTML: https://mlsanity.com/paper/1802.05400/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 30 of 40)

- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization](https://mlsanity.com/paper/2210.01628/) (2022)
- [Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?](https://mlsanity.com/paper/2301.12844/) (2023)
- [A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences](https://mlsanity.com/paper/2406.04739/) (2024)
- [B2Opt: Learning to Optimize Black-box Optimization with Little Budget](https://mlsanity.com/paper/2304.11787/) (2023)
- [Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)](https://mlsanity.com/paper/2208.13771/) (2022)
- [We Still Don't Understand High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2512.00170/) (2025)
- [Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2305.19838/) (2023)
- [Improving sample efficiency of high dimensional Bayesian optimization with MCMC](https://mlsanity.com/paper/2401.02650/) (2024)
- [Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing](https://mlsanity.com/paper/2110.01387/) (2021)
- [A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models](https://mlsanity.com/paper/2103.08026/) (2021)
- [High Dimensional Bayesian Optimization with Kernel Principal Component Analysis](https://mlsanity.com/paper/2204.13753/) (2022)
- [Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2302.08298/) (2023)
- [A Bayesian Optimization approach for calibrating large-scale activity-based transport models](https://mlsanity.com/paper/2302.03480/) (2023)
- [BOIDS: High-dimensional Bayesian Optimization via Incumbent-guided Direction Lines and Subspace Embeddings](https://mlsanity.com/paper/2412.12918/) (2024)
- [Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems](https://mlsanity.com/paper/2412.20350/) (2024)
- [High Dimensional Bayesian Optimization using Lasso Variable Selection](https://mlsanity.com/paper/2504.01743/) (2025)
- [High dimensional Bayesian Optimization Algorithm for Complex System in Time Series](https://mlsanity.com/paper/2108.02289/) (2021)
- [Expected Coordinate Improvement for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2404.11917/) (2024)
- [Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach](https://mlsanity.com/paper/2410.23148/) (2024)
- [High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces](https://mlsanity.com/paper/2412.16554/) (2024)
- [Enhancing Trust-Region Bayesian Optimization via Newton Methods](https://mlsanity.com/paper/2508.18423/) (2025)
- [A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search](https://mlsanity.com/paper/2602.11171/) (2026)
- [Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers](https://mlsanity.com/paper/2108.05969/) (2021)
- [High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series](https://mlsanity.com/paper/2201.00147/) (2022)
- [A model aggregation approach for high-dimensional large-scale optimization](https://mlsanity.com/paper/2205.07525/) (2022)
- [Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning](https://mlsanity.com/paper/2309.04510/) (2023)
- [An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization](https://mlsanity.com/paper/2403.05425/) (2024)

## Abstract (arXiv)

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited active variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize only a subset of variables at each iteration. We derive theoretical bounds for the regret and show how it can inform the derivation of our algorithm. We demonstrate the efficacy of our algorithms for optimization on two benchmark functions and two real-world applications- training cascade classifiers and optimizing alloy composition.