arXiv subjects

Mujin Cheon

Publications and source records attributed to Mujin Cheon.

3 recordsLinked to original sources

arXiv2026

How Many Initial Points Does Bayesian Optimization Need?

Bayesian Optimization (BO) generally begins with an initialization phase: a batch of $n_0$ uninformed evaluations. The choice of $n_0$ remains largely heuristic, and we empirically observe that the total cost (random initial points plus BO iterations needed to find the global optimum) is U-shaped in $n_0$, i.e., a practitioner wastes resources by selecting either too low or too high a value of $n_0$. We find this tradeoff persists across MLE, Bayesian MCMC, and exact GP hyperparameters, as well as across acquisition functions. Toward the latter, Thompson Sampling appears an exception, with both total cost and simple regret essentially $n_0$-agnostic, though higher in our experiments. We attribute this U-shape to the known boundary issue of variance-driven BO: BO burns early budget on corners of the hypercube before turning inward. We demonstrate this effect using a 3D BO trajectory where the exact hyperparameters are known. We conclude with practical recommendations: use multi-step lookahead BO where possible; otherwise use Thompson Sampling when $n_0$ cannot be tuned, and a generously large $n_0$ when it can.

cs.LG↗

arXiv2025

BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization

The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functions. This presents a significant practical challenge: an inappropriate combination of these can lead to poor performance and wasted evaluations. While individual improvements to kernel functions and acquisition functions have been actively explored, the joint and autonomous selection of the best pair of these fundamental hyperparameters has been largely overlooked. This forced practitioners to rely on heuristics or costly manual training. In this work, we propose a framework, BOOST (Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique), that automates this selection. BOOST utilizes a simple offline evaluation stage to predict the performance of various kernel-acquisition function pairs and identify the most promising pair before committing to the expensive evaluation process. BOOST is a data-driven strategy selection procedure that evaluates kernel-acquisition pairs based on their empirical performance on the data-in-hand. At each iteration, previously observed points are partitioned into a reference set and a query set. These subsets play roles analogous to training and validation sets in machine learning: the reference set is used for model construction, while the query set represents unseen regions to retrospectively evaluate how effectively each candidate strategy progresses toward the target value. Experiments on synthetic benchmarks and machine learning hyperparameter optimization tasks demonstrate that BOOST consistently improves over fixed-hyperparameter BO and remains competitive with state-of-the-art adaptive methods, highlighting its robustness across diverse landscapes.

cs.LG↗

arXiv2024

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization

To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated promising results in recent years. However, owing to the curse of dimensionality, most of these methods make significant approximations or suffer scalability issues. This paper presents a novel reinforcement learning (RL)-based framework for multi-step lookahead BO in high-dimensional black-box optimization problems. The proposed method enhances the scalability and decision-making quality of multi-step lookahead BO by efficiently solving the sequential dynamic program of the BO process in a near-optimal manner using RL. We first introduce an Attention-DeepSets encoder to represent the state of knowledge to the RL agent and subsequently propose a multi-task, fine-tuning procedure based on end-to-end (encoder-RL) on-policy learning. We evaluate the proposed method, EARL-BO (Encoder Augmented RL for BO), on synthetic benchmark functions and hyperparameter tuning problems, finding significantly improved performance compared to existing multi-step lookahead and high-dimensional BO methods.

cs.LG↗