Efficient computation of the Knowledge Gradient for Bayesian Optimization

• URL: http://arxiv.org/abs/2209.15367v1
• Date: Fri, 30 Sep 2022 10:39:38 GMT
• Title: Efficient computation of the Knowledge Gradient for Bayesian Optimization
• Authors: Juan Ungredda and Michael Pearce and Juergen Branke
• Abstract summary: One-shot Hybrid KG is a new approach that combines several of the previously proposed ideas and is cheap to compute as well as powerful and efficient. All experiments are implemented in BOTorch and show empirically drastically reduced computational overhead with equal or improved performance.
• Score: 1.0497128347190048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions. One key component of a Bayesian optimization algorithm is the acquisition function that determines which solution should be evaluated in every iteration. A popular and very effective choice is the Knowledge Gradient acquisition function, however there is no analytical way to compute it. Several different implementations make different approximations. In this paper, we review and compare the spectrum of Knowledge Gradient implementations and propose One-shot Hybrid KG, a new approach that combines several of the previously proposed ideas and is cheap to compute as well as powerful and efficient. We prove the new method preserves theoretical properties of previous methods and empirically show the drastically reduced computational overhead with equal or improved performance. All experiments are implemented in BOTorch and code is available on github.

Related papers

• Principled Preferential Bayesian Optimization [22.269732173306192]
  We study the problem of preferential Bayesian optimization (BO) We aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. An optimistic algorithm with an efficient computational method is then developed to solve the problem.
  arXiv  Detail & Related papers  (2024-02-08T02:57:47Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• Batch Bayesian Optimization via Particle Gradient Flows [0.5735035463793008]
  We show how to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. We construct a new function based on multipoint expected probability which is over the space of probability measures.
  arXiv  Detail & Related papers  (2022-09-10T18:10:15Z)
• Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [78.78486761923855]
  In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. We present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm.
  arXiv  Detail & Related papers  (2021-04-19T17:22:11Z)
• Revisiting Bayesian Optimization in the light of the COCO benchmark [1.4467794332678539]
  This article reports a large investigation about the effects on the performance of (Gaussian process based) BO of common and less common design choices. The code developed for this study makes the new version (v2.1.1) of the R package DiceOptim available on CRAN.
  arXiv  Detail & Related papers  (2021-03-30T19:45:18Z)
• Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework [100.36569795440889]
  This work is on the iteration of zero-th-order (ZO) optimization which does not require first-order information. We show that with a graceful design in coordinate importance sampling, the proposed ZO optimization method is efficient both in terms of complexity as well as as function query cost.
  arXiv  Detail & Related papers  (2020-12-21T17:29:58Z)
• Divide and Learn: A Divide and Conquer Approach for Predict+Optimize [50.03608569227359]
  The predict+optimize problem combines machine learning ofproblem coefficients with a optimization prob-lem that uses the predicted coefficients. We show how to directlyexpress the loss of the optimization problem in terms of thepredicted coefficients as a piece-wise linear function. We propose a novel divide and algorithm to tackle optimization problems without this restriction and predict itscoefficients using the optimization loss.
  arXiv  Detail & Related papers  (2020-12-04T00:26:56Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• Resource Aware Multifidelity Active Learning for Efficient Optimization [0.8717253904965373]
  This paper introduces the Resource Aware Active Learning (RAAL) strategy to accelerate the optimization of black box functions. The RAAL strategy optimally seeds multiple points at each allowing for a major speed up of the optimization task.
  arXiv  Detail & Related papers  (2020-07-09T10:01:32Z)

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.