Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

• URL: http://arxiv.org/abs/2211.01053v1
• Date: Wed, 2 Nov 2022 11:37:06 GMT
• Title: Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
• Authors: Paul E. Chang, Prakhar Verma, ST John, Victor Picheny, Henry Moss and Arno Solin
• Abstract summary: We focus on fantasizing' batch acquisition functions that need the ability to condition on new fantasized data. By using a sparse Dual GP parameterization, we gain linear scaling with batch size as well as one-step updates for non-Gaussian likelihoods.
• Score: 14.050425158209826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian likelihood. In this paper, we focus on `fantasizing' batch acquisition functions that need the ability to condition on new fantasized data computationally efficiently. By using a sparse Dual GP parameterization, we gain linear scaling with batch size as well as one-step updates for non-Gaussian likelihoods, thus extending sparse models to greedy batch fantasizing acquisition functions.

Related papers

• 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)
• BatchGFN: Generative Flow Networks for Batch Active Learning [80.73649229919454]
  BatchGFN is a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward. We show our approach enables principled sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems.
  arXiv  Detail & Related papers  (2023-06-26T20:41:36Z)
• Surrogate modeling for Bayesian optimization beyond a single Gaussian process [62.294228304646516]
  We propose a novel Bayesian surrogate model to balance exploration with exploitation of the search space. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret.
  arXiv  Detail & Related papers  (2022-05-27T16:43:10Z)
• Bayesian Active Learning with Fully Bayesian Gaussian Processes [0.0]
  In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient querying. We show that incorporating the bias-variance trade-off in the acquisition functions mitigates unnecessary and expensive data labeling.
  arXiv  Detail & Related papers  (2022-05-20T13:52:04Z)
• Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes [0.0]
  We adapt the Vecchia approximation, a popular GP approximation from spatial statistics, to enable scalable high-dimensional Bayesian optimization. We focus on the use of our warped Vecchia GP in trust-region Bayesian optimization via Thompson sampling.
  arXiv  Detail & Related papers  (2022-03-02T23:55:14Z)
• Non-Gaussian Gaussian Processes for Few-Shot Regression [71.33730039795921]
  We propose an invertible ODE-based mapping that operates on each component of the random variable vectors and shares the parameters across all of them. NGGPs outperform the competing state-of-the-art approaches on a diversified set of benchmarks and applications.
  arXiv  Detail & Related papers  (2021-10-26T10:45:25Z)
• Transforming Gaussian Processes With Normalizing Flows [15.886048234706633]
  We show that a parametric invertible transformation can be made input-dependent and encode interpretable prior knowledge. We derive a variational approximation to the resulting inference problem, which is as fast as variational GP regression. The resulting algorithm's computational and inferential performance is excellent, and we demonstrate this on a range of data sets.
  arXiv  Detail & Related papers  (2020-11-03T09:52:37Z)
• Likelihood-Free Inference with Deep Gaussian Processes [70.74203794847344]
  Surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. We propose a Deep Gaussian Process (DGP) surrogate model that can handle more irregularly behaved target distributions. Our experiments show how DGPs can outperform GPs on objective functions with multimodal distributions and maintain a comparable performance in unimodal cases.
  arXiv  Detail & Related papers  (2020-06-18T14:24:05Z)
• 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.