Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization

• URL: http://arxiv.org/abs/2407.00610v2
• Date: Thu, 03 Oct 2024 09:44:32 GMT
• Title: Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
• Authors: Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu,
• Abstract summary: Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle in a sample-efficient way. Recent inverse modeling approaches that map objective space to the design space with conditional diffusion models have demonstrated impressive capability in learning the data manifold. We propose Diff-BBO, an inverse approach leveraging diffusion models for online BBO problem.
• Score: 20.45482366024264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle in a sample-efficient way. While prior studies focus on forward approaches to learn surrogates for the unknown objective function, they struggle with steering clear of out-of-distribution and invalid inputs. Recently, inverse modeling approaches that map objective space to the design space with conditional diffusion models have demonstrated impressive capability in learning the data manifold. They have shown promising performance in offline BBO tasks. However, these approaches require a pre-collected dataset. How to design the acquisition function for inverse modeling to actively query new data remains an open question. In this work, we propose diffusion-based inverse modeling for black-box optimization (Diff-BBO), an inverse approach leveraging diffusion models for online BBO problem. Instead of proposing candidates in the design space, Diff-BBO employs a novel acquisition function Uncertainty-aware Exploration (UaE) to propose objective function values. Subsequently, we employ a conditional diffusion model to generate samples based on these proposed values within the design space. We demonstrate that using UaE results in optimal optimization outcomes, supported by both theoretical and empirical evidence.

Related papers

• Test-time Alignment of Diffusion Models without Reward Over-optimization [8.981605934618349]
  Diffusion models excel in generative tasks, but aligning them with specific objectives remains challenging. We propose a training-free, test-time method based on Sequential Monte Carlo (SMC) to sample from the reward-aligned target distribution. We demonstrate its effectiveness in single-reward optimization, multi-objective scenarios, and online black-box optimization.
  arXiv  Detail & Related papers  (2025-01-10T09:10:30Z)
• Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
  Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving. We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance. Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
  arXiv  Detail & Related papers  (2024-08-01T17:59:59Z)
• Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation [60.41803046775034]
  We show how to perform user-preferred targeted generation via diffusion models with only black-box target scores of users. Experiments on both numerical test problems and target-guided 3D-molecule generation tasks show the superior performance of our method in achieving better target scores.
  arXiv  Detail & Related papers  (2024-06-02T17:26:27Z)
• Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space [65.44449711359724]
  High-dimensional and highly-multimodal input design space of black-box function pose inherent challenges for existing methods. We consider finding a latent space that serves as a compressed yet accurate representation of the design-value joint space. We propose Noise-intensified Telescoping density-Ratio Estimation scheme for variational learning of an accurate latent space model.
  arXiv  Detail & Related papers  (2024-05-27T00:11:53Z)
• Variational Schrödinger Diffusion Models [14.480273869571468]
  Schr"odinger bridge (SB) has emerged as the go-to method for optimizing transportation plans in diffusion models. We leverage variational inference to linearize the forward score functions (variational scores) of SB. We propose the variational Schr"odinger diffusion model (VSDM), where the forward process is a multivariate diffusion and the variational scores are adaptively optimized for efficient transport.
  arXiv  Detail & Related papers  (2024-05-08T04:01:40Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints [42.47298301874283]
  We propose to perform optimization within the data manifold using diffusion models. Depending on the differentiability of the objective function, we propose two different sampling methods. Our method achieves better or comparable performance with previous state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-28T03:09:12Z)
• Simulation Based Bayesian Optimization [0.6526824510982799]
  This paper introduces Simulation Based Bayesian Optimization (SBBO) as a novel approach to optimizing acquisition functions. SBBO allows the use of surrogate models tailored for spaces with discrete variables. We demonstrate empirically the effectiveness of SBBO method using various choices of surrogate models.
  arXiv  Detail & Related papers  (2024-01-19T16:56:11Z)
• Predictive Modeling through Hyper-Bayesian Optimization [60.586813904500595]
  We propose a novel way of integrating model selection and BO for the single goal of reaching the function optima faster. The algorithm moves back and forth between BO in the model space and BO in the function space, where the goodness of the recommended model is captured. In addition to improved sample efficiency, the framework outputs information about the black-box function.
  arXiv  Detail & Related papers  (2023-08-01T04:46:58Z)
• Protein Design with Guided Discrete Diffusion [67.06148688398677]
  A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. We propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods.
  arXiv  Detail & Related papers  (2023-05-31T16:31:24Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)
• Flexible Amortized Variational Inference in qBOLD MRI [56.4324135502282]
  Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. Existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV.
  arXiv  Detail & Related papers  (2022-03-11T10:47:16Z)
• Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces [27.989924313988016]
  We consider the problem of optimizing spaces (e.g., sequences, trees, and graphs) using expensive black-box function evaluations. A recent BO approach for spaces is through a reduction to BO over continuous spaces by learning a latent representation of structures. This paper proposes a principled approach referred as LADDER to overcome this drawback.
  arXiv  Detail & Related papers  (2021-11-01T18:26:22Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)

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.