Functional Graphical Models: Structure Enables Offline Data-Driven Optimization

• URL: http://arxiv.org/abs/2401.05442v2
• Date: Fri, 12 Jan 2024 02:49:17 GMT
• Title: Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
• Authors: Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine
• Abstract summary: 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.
• Score: 121.57202302457135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of proteins and their corresponding fluorescence levels, we might want to optimize for a new protein with the highest possible fluorescence. This kind of data-driven optimization (DDO) presents a range of challenges beyond those in standard prediction problems, since we need models that successfully predict the performance of new designs that are better than the best designs seen in the training set. It is not clear theoretically when existing approaches can even perform better than the naive approach that simply selects the best design in the dataset. In this paper, we study how structure can enable sample-efficient data-driven optimization. To formalize the notion of structure, we introduce functional graphical models (FGMs) and show theoretically how they can provide for principled data-driven optimization by decomposing the original high-dimensional optimization problem into smaller sub-problems. This allows us to derive much more practical regret bounds for DDO, and the result implies that DDO with FGMs can achieve nearly optimal designs in situations where naive approaches fail due to insufficient coverage of the offline data. We further present a data-driven optimization algorithm that inferes the FGM structure itself, either over the original input variables or a latent variable representation of the inputs.

Related papers

• Diffusion Models as Network Optimizers: Explorations and Analysis [71.69869025878856]
  generative diffusion models (GDMs) have emerged as a promising new approach to network optimization. In this study, we first explore the intrinsic characteristics of generative models. We provide a concise theoretical and intuitive demonstration of the advantages of generative models over discriminative network optimization.
  arXiv  Detail & Related papers  (2024-11-01T09:05:47Z)
• From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization [30.689032197123755]
  This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of offline" data examples. Instead of learning and then optimizing the unknown objective function, we take on a less intuitive but more direct view that optimization can be thought of as a process of sampling from a generative model.
  arXiv  Detail & Related papers  (2024-01-04T01:32:50Z)
• Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization [16.57676001669012]
  In data-driven optimization, the sample performance of the obtained decision typically incurs an optimistic bias against the true performance. Common techniques to correct this bias, such as cross-validation, require repeatedly solving additional optimization problems and are therefore expensive. We develop a general bias correction approach that directly approximates the first-order bias and does not require solving any additional optimization problems.
  arXiv  Detail & Related papers  (2023-06-16T07:07:58Z)
• Data-informed Deep Optimization [3.331457049134526]
  We propose a data-informed deep optimization (DiDo) approach to solve high-dimensional design problems. We use a deep neural network (DNN) to learn the feasible region and to sample feasible points for fitting the objective function. Our results indicate that the DiDo approach empowered by DNN is flexible and promising for solving general high-dimensional design problems in practice.
  arXiv  Detail & Related papers  (2021-07-17T02:53:54Z)
• 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)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)
• 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)
• Model Inversion Networks for Model-Based Optimization [110.24531801773392]
  We propose model inversion networks (MINs), which learn an inverse mapping from scores to inputs. MINs can scale to high-dimensional input spaces and leverage offline logged data for both contextual and non-contextual optimization problems. We evaluate MINs on tasks from the Bayesian optimization literature, high-dimensional model-based optimization problems over images and protein designs, and contextual bandit optimization from logged data.
  arXiv  Detail & Related papers  (2019-12-31T18:06:49Z)

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.