An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization

• URL: http://arxiv.org/abs/2504.11353v1
• Date: Tue, 15 Apr 2025 16:23:25 GMT
• Title: An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
• Authors: Jundi Huang, Dawei Zhan,
• Abstract summary: We propose AdaDropout to tackle high-dimensional challenges and improve solution quality.<n>It achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensionality of the acquisition function. In the proposed algorithm, we adaptively dropout the variables of the acquisition function along the iterations. By gradually reducing the dimension of the acquisition function, the proposed approach has less and less difficulty to optimize the acquisition function. Numerical experiments demonstrate that AdaDropout effectively tackle high-dimensional challenges and improve solution quality where standard Bayesian optimization methods often struggle. Moreover, it achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches. This work provides a simple yet efficient solution for high-dimensional expensive optimization.

Related papers

• High Dimensional Bayesian Optimization using Lasso Variable Selection [9.051539805042651]
  We introduce a novel method that identifies important variables by estimating the length scales of Gaussian process kernels.<n>We demonstrate that our proposed method achieves cumulative regret with a sublinear growth rate in the worst case.<n> Experiments on high-dimensional synthetic functions and real-world problems show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-04-02T13:54:04Z)
• Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization [66.51747366239299]
  We propose a smooth variant of the min-max problem based on the augmented Lagrangian.<n>The proposed algorithm scales better with the number of objectives than subgradient-based strategies.
  arXiv  Detail & Related papers  (2025-03-16T11:05:51Z)
• High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings [0.6291443816903801]
  This paper proposes a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension.<n>The resulting BO method combines in an adaptive way both random and supervised linear embeddings.<n>The obtained results show the high potential of EGORSE to solve high-dimensional blackbox optimization problems.
  arXiv  Detail & Related papers  (2025-02-02T16:57:05Z)
• Expected Coordinate Improvement for High-Dimensional Bayesian Optimization [0.0]
  We propose the expected coordinate improvement (ECI) criterion for high-dimensional Bayesian optimization.<n>The proposed approach selects the coordinate with the highest ECI value to refine in each iteration and covers all the coordinates gradually by iterating over the coordinates.<n> Numerical experiments show that the proposed algorithm can achieve significantly better results than the standard BO algorithm and competitive results when compared with five state-of-the-art high-dimensional BOs.
  arXiv  Detail & Related papers  (2024-04-18T05:48:15Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• High dimensional Bayesian Optimization Algorithm for Complex System in Time Series [1.9371782627708491]
  This paper presents a novel high dimensional Bayesian optimization algorithm. Based on the time-dependent or dimension-dependent characteristics of the model, the proposed algorithm can reduce the dimension evenly. To increase the final accuracy of the optimal solution, the proposed algorithm adds a local search based on a series of Adam-based steps at the final stage.
  arXiv  Detail & Related papers  (2021-08-04T21:21:17Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)
• Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization [0.1749935196721634]
  We use the addition-ality of the objective function into mapping both the kernel and the acquisition function of the Bayesian Optimization. This ap-proach makes more efficient the learning/updating of the probabilistic surrogate model. Results are presented for real-life application, that is the control of pumps in urban water distribution systems.
  arXiv  Detail & Related papers  (2020-03-09T15:45:57Z)

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.