Gradient-based Sample Selection for Faster Bayesian Optimization

• URL: http://arxiv.org/abs/2504.07742v1
• Date: Thu, 10 Apr 2025 13:38:15 GMT
• Title: Gradient-based Sample Selection for Faster Bayesian Optimization
• Authors: Qiyu Wei, Haowei Wang, Zirui Cao, Songhao Wang, Richard Allmendinger, Mauricio A Álvarez,
• Abstract summary: In large-budget scenarios, directly employing the standard GP model faces significant challenges in computational time and resource requirements.<n>We propose a novel approach, gradient-based sample selection Bayesian Optimization (GSSBO), to enhance the computational efficiency of BO.<n>Our approach significantly reduces the computational cost of GP fitting in BO while maintaining optimization performance comparable to baseline methods.
• Score: 11.242721310713963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity in computing the Gaussian process (GP) surrogate model. In large-budget scenarios, directly employing the standard GP model faces significant challenges in computational time and resource requirements. In this paper, we propose a novel approach, gradient-based sample selection Bayesian Optimization (GSSBO), to enhance the computational efficiency of BO. The GP model is constructed on a selected set of samples instead of the whole dataset. These samples are selected by leveraging gradient information to maintain diversity and representation. We provide a theoretical analysis of the gradient-based sample selection strategy and obtain explicit sublinear regret bounds for our proposed framework. Extensive experiments on synthetic and real-world tasks demonstrate that our approach significantly reduces the computational cost of GP fitting in BO while maintaining optimization performance comparable to baseline methods.

Related papers

• Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes [8.40647440727154]
  We argue that Bayesian optimization algorithms with sparse GPs can more efficiently allocate their representational power to relevant regions of the search space.<n>We show that FocalBO can efficiently leverage large amounts of offline and online data to achieve state-of-the-art performance on robot morphology design and to control a 585-dimensional musculoskeletal system.
  arXiv  Detail & Related papers  (2024-12-29T06:36:15Z)
• Vector Optimization with Gaussian Process Bandits [7.049738935364297]
  Learning problems in which multiple objectives must be considered simultaneously often arise in various fields, including engineering, drug design, and environmental management.<n>Traditional methods for dealing with multiple black-box objective functions have limitations in incorporating objective preferences and exploring the solution space accordingly.<n>We propose Vector Optimization with Gaussian Process (VOGP), a probably approximately correct adaptive elimination algorithm that performs black-box vector optimization using Gaussian process bandits.
  arXiv  Detail & Related papers  (2024-12-03T14:47:46Z)
• Gaussian Process Thompson Sampling via Rootfinding [2.94944680995069]
  Thompson sampling (TS) is a simple, effective policy in Bayesian decision making. In continuous optimization, the posterior of the objective function is often a Gaussian process (GP), whose sample paths have numerous local optima. We introduce an efficient global optimization strategy for GP-TS that carefully selects starting points for gradient-based multi-starts.
  arXiv  Detail & Related papers  (2024-10-10T16:06:45Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling [0.0]
  A sample-efficient approach to solving multiobjective optimization is via process oracle (GP) surrogates and MOBOOTS$.<n>We propose a Thompson sampling (TS) based approach ($qtextttPOTS$)<n>$qtextttPOTS$ solves a cheap multiobjective optimization on the GP posteriors with evolutionary approaches.
  arXiv  Detail & Related papers  (2023-10-24T12:35:15Z)
• Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation [44.53678257757108]
  We propose a new BO method that can sub-linearly converge to the objective function's global optimum. Our method uses a multi-armed bandit technique (EXP3) to add random data points to the BO process. We demonstrate empirically that our method outperforms existing approaches on various synthetic and real-world problems.
  arXiv  Detail & Related papers  (2023-06-12T03:35:45Z)
• Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis [11.143778114800272]
  We show that a dynamic selection of the AF can benefit the BO design. We pave a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.
  arXiv  Detail & Related papers  (2022-11-17T17:15:04Z)
• Surrogate modeling for Bayesian optimization beyond a single Gaussian process [62.294228304646516]
  We propose a novel Bayesian surrogate model to balance exploration with exploitation of the search space. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret.
  arXiv  Detail & Related papers  (2022-05-27T16:43:10Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework [100.36569795440889]
  This work is on the iteration of zero-th-order (ZO) optimization which does not require first-order information. We show that with a graceful design in coordinate importance sampling, the proposed ZO optimization method is efficient both in terms of complexity as well as as function query cost.
  arXiv  Detail & Related papers  (2020-12-21T17:29:58Z)
• An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data [68.8204255655161]
  An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way. Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
  arXiv  Detail & Related papers  (2020-12-11T14:33:27Z)
• Optimal Bayesian experimental design for subsurface flow problems [77.34726150561087]
  We propose a novel approach for development of chaos expansion (PCE) surrogate model for the design utility function. This novel technique enables the derivation of a reasonable quality response surface for the targeted objective function with a computational budget comparable to several single-point evaluations.
  arXiv  Detail & Related papers  (2020-08-10T09:42:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.