ROMO: Retrieval-enhanced Offline Model-based Optimization

• URL: http://arxiv.org/abs/2310.07560v2
• Date: Thu, 19 Oct 2023 06:04:37 GMT
• Title: ROMO: Retrieval-enhanced Offline Model-based Optimization
• Authors: Mingcheng Chen, Haoran Zhao, Yuxiang Zhao, Hulei Fan, Hongqiao Gao, Yong Yu, Zheng Tian
• Abstract summary: Data-driven black-box model-based optimization (MBO) problems arise in a number of practical application scenarios. We propose retrieval-enhanced offline model-based optimization (ROMO) ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting.
• Score: 14.277672372460785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset. In this work, we consider a more general but challenging MBO setting, named constrained MBO (CoMBO), where only part of the design space can be optimized while the rest is constrained by the environment. A new challenge arising from CoMBO is that most observed designs that satisfy the constraints are mediocre in evaluation. Therefore, we focus on optimizing these mediocre designs in the offline dataset while maintaining the given constraints rather than further boosting the best observed design in the traditional MBO setting. We propose retrieval-enhanced offline model-based optimization (ROMO), a new derivable forward approach that retrieves the offline dataset and aggregates relevant samples to provide a trusted prediction, and use it for gradient-based optimization. ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting. Empirically, we conduct experiments on a synthetic Hartmann (3D) function dataset, an industrial CIO dataset, and a suite of modified tasks in the Design-Bench benchmark. Results show that ROMO performs well in a wide range of constrained optimization tasks.

Related papers

• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  We develop a model that learns the structure of an MBO task and empirically leads to improved designs. We evaluate Cliqueformer on various tasks, ranging from high-dimensional black-box functions to real-world tasks of chemical and genetic design.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient [52.2669490431145]
  PropEn is inspired by'matching', which enables implicit guidance without training a discriminator. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution.
  arXiv  Detail & Related papers  (2024-05-28T11:30:19Z)
• 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)
• Generative Adversarial Model-Based Optimization via Source Critic Regularization [25.19579059511105]
  We propose generative adversarial model-based optimization using adaptive source critic regularization (aSCR) ASCR constrains the optimization trajectory to regions of the design space where the surrogate function is reliable. We show how leveraging aSCR with standard Bayesian optimization outperforms existing methods on a suite of offline generative design tasks.
  arXiv  Detail & Related papers  (2024-02-09T16:43:57Z)
• Functional Graphical Models: Structure Enables Offline Data-Driven Optimization [111.28605744661638]
  We show how structure can enable sample-efficient data-driven optimization. We also present a data-driven optimization algorithm that infers the FGM structure itself.
  arXiv  Detail & Related papers  (2024-01-08T22:33:14Z)
• Conflict-Averse Gradient Optimization of Ensembles for Effective Offline Model-Based Optimization [0.0]
  We evaluate two algorithms for combining gradient information: multiple gradient descent algorithm (MGDA) and conflict-averse gradient descent (CAGrad) Our results suggest that MGDA and CAGrad strike a desirable balance between conservatism and optimality and can help robustify data-driven offline MBO without compromising optimality of designs.
  arXiv  Detail & Related papers  (2023-03-31T10:00:27Z)
• 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)
• Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization [82.02008764719896]
  Black-box model-based optimization problems are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, aircraft, and robots. We present Design-Bench, a benchmark for offline MBO with a unified evaluation protocol and reference implementations of recent methods. Our benchmark includes a suite of diverse and realistic tasks derived from real-world optimization problems in biology, materials science, and robotics.
  arXiv  Detail & Related papers  (2022-02-17T05:33:27Z)
• 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.