LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions

• URL: http://arxiv.org/abs/2311.11328v2
• Date: Sun, 16 Jun 2024 10:22:52 GMT
• Title: LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions
• Authors: E. Visser, C. E. van Daalen, J. C. Schoeman,
• Abstract summary: We propose the LABCAT algorithm, which extends trust-region-based BO. We show that the algorithm outperforms several state-of-the-art BO and other black-box optimization algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian optimization (BO) is a popular method for optimizing expensive black-box functions. BO has several well-documented shortcomings, including computational slowdown with longer optimization runs, poor suitability for non-stationary or ill-conditioned objective functions, and poor convergence characteristics. Several algorithms have been proposed that incorporate local strategies, such as trust regions, into BO to mitigate these limitations; however, none address all of them satisfactorily. To address these shortcomings, we propose the LABCAT algorithm, which extends trust-region-based BO by adding a rotation aligning the trust region with the weighted principal components and an adaptive rescaling strategy based on the length-scales of a local Gaussian process surrogate model with automatic relevance determination. Through extensive numerical experiments using a set of synthetic test functions and the well-known COCO benchmarking software, we show that the LABCAT algorithm outperforms several state-of-the-art BO and other black-box optimization algorithms.

Related papers

• High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy [16.521207412129833]
  We propose a novel technique for defining the local regions using the Covariance Matrix Adaptation (CMA) strategy. Based on this search distribution, we then define the local regions consisting of data points with high probabilities of being the global optimum. Our approach serves as a meta-algorithm as it can incorporate existing black-box BOs, such as BO, TuRBO, and BAxUS, to find the global optimum.
  arXiv  Detail & Related papers  (2024-02-05T15:32:10Z)
• 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)
• Advancing Bayesian Optimization via Learning Correlated Latent Space [15.783344085533187]
  We propose Correlated latent space Bayesian Optimization (CoBO), which focuses on learning correlated latent spaces. Specifically, our method introduces Lipschitz regularization, loss weighting, and trust region recoordination to minimize the inherent gap around the promising areas. We demonstrate the effectiveness of our approach on several optimization tasks in discrete data, such as molecule design and arithmetic expression fitting.
  arXiv  Detail & Related papers  (2023-10-31T08:24:41Z)
• Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation [84.0621253654014]
  We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
  arXiv  Detail & Related papers  (2023-07-25T09:45:47Z)
• Model-based Causal Bayesian Optimization [78.120734120667]
  We propose model-based causal Bayesian optimization (MCBO) MCBO learns a full system model instead of only modeling intervention-reward pairs. Unlike in standard Bayesian optimization, our acquisition function cannot be evaluated in closed form.
  arXiv  Detail & Related papers  (2022-11-18T14:28:21Z)
• Batch Bayesian optimisation via density-ratio estimation with guarantees [26.052368583196426]
  We present a theoretical analysis of BORE's regret and an extension of the algorithm with improved uncertainty estimates. We also show that BORE can be naturally extended to a batch optimisation setting by recasting the problem as approximate Bayesian inference.
  arXiv  Detail & Related papers  (2022-09-22T00:42:18Z)
• Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces [54.58348769621782]
  Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
  arXiv  Detail & Related papers  (2022-07-02T16:59:37Z)
• Sparse Bayesian Optimization [16.867375370457438]
  We present several regularization-based approaches that allow us to discover sparse and more interpretable configurations. We propose a novel differentiable relaxation based on homotopy continuation that makes it possible to target sparsity. We show that we are able to efficiently optimize for sparsity.
  arXiv  Detail & Related papers  (2022-03-03T18:25:33Z)
• Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [78.78486761923855]
  In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. We present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm.
  arXiv  Detail & Related papers  (2021-04-19T17:22:11Z)
• TREGO: a Trust-Region Framework for Efficient Global Optimization [63.995130144110156]
  We propose and analyze a trust-region-like EGO method (TREGO) TREGO alternates between regular EGO steps and local steps within a trust region. Our algorithm enjoys strong global convergence properties, while departing from EGO only for a subset of optimization steps.
  arXiv  Detail & Related papers  (2021-01-18T00:14:40Z)
• Scalable Constrained Bayesian Optimization [10.820024633762596]
  The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and the scientific community. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that overcomes the above challenges and pushes the state-the-art.
  arXiv  Detail & Related papers  (2020-02-20T01:48:46Z)

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.