Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation

• URL: http://arxiv.org/abs/2206.02437v1
• Date: Mon, 6 Jun 2022 08:56:56 GMT
• Title: Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
• Authors: Henry B. Moss, Sebastian W. Ober, Victor Picheny
• Abstract summary: We propose a novel inducing point design that uses a principled information-theoretic criterion to select inducing points. By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high- throughput BO.
• Score: 9.732863739456036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representative subsets of the available data to build approximate posteriors, sparse models dramatically reduce the computational costs of surrogate modelling by relying on a small set of pseudo-observations, the so-called inducing points, in lieu of the full data set. However, current approaches to design inducing points are not appropriate within BO loops as they seek to reduce global uncertainty in the objective function. Thus, the high-fidelity modelling of promising and data-dense regions required for precise optimisation is sacrificed and computational resources are instead wasted on modelling areas of the space already known to be sub-optimal. Inspired by entropy-based BO methods, we propose a novel inducing point design that uses a principled information-theoretic criterion to select inducing points. By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high-throughput BO.

Related papers

• Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability [73.34532767873785]
  We propose the concept of Information Density'' (ID) to indicate whether a matrix strongly belongs to certain feature spaces. We introduce the Dense Information Prompt (DIP) to enhance information density to improve generalization. DIP significantly reduces the number of tunable parameters and the requisite storage space, making it particularly advantageous in resource-constrained settings.
  arXiv  Detail & Related papers  (2023-12-17T20:42:43Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)
• A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces [0.0]
  We propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics.
  arXiv  Detail & Related papers  (2023-09-28T14:25:14Z)
• 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)
• Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows [54.050498411883495]
  We develop a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows. We show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the textitPR-divergences. Our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet.
  arXiv  Detail & Related papers  (2023-05-30T10:07:17Z)
• Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation [9.732863739456036]
  We show that existing methods for allocating inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy for use in BO.
  arXiv  Detail & Related papers  (2023-01-24T16:43:29Z)
• Bayesian Optimization with Informative Covariance [13.113313427848828]
  We propose novel informative covariance functions for optimization, leveraging nonstationarity to encode preferences for certain regions of the search space. We demonstrate that the proposed functions can increase the sample efficiency of Bayesian optimization in high dimensions, even under weak prior information.
  arXiv  Detail & Related papers  (2022-08-04T15:05:11Z)
• Approximate Bayesian Optimisation for Neural Networks [6.921210544516486]
  A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. The necessity to solve the analytical tractability and the computational feasibility in a idealistic fashion enables to ensure the efficiency and the applicability.
  arXiv  Detail & Related papers  (2021-08-27T19:03:32Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Bayesian Optimization Meets Laplace Approximation for Robotic Introspection [41.117361086267806]
  We introduce a scalable Laplace Approximation (LA) technique to make Deep Neural Networks (DNNs) more introspective. In particular, we propose a novel Bayesian Optimization (BO) algorithm to mitigate their tendency of under-fitting the true weight posterior. We show that the proposed framework can be scaled up to large datasets and architectures.
  arXiv  Detail & Related papers  (2020-10-30T09:28:10Z)
• Global Optimization of Gaussian processes [52.77024349608834]
  We propose a reduced-space formulation with trained Gaussian processes trained on few data points. The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding. In total, we reduce time convergence by orders of orders of the proposed method.
  arXiv  Detail & Related papers  (2020-05-21T20:59:11Z)

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.