MUMBO: MUlti-task Max-value Bayesian Optimization

• URL: http://arxiv.org/abs/2006.12093v1
• Date: Mon, 22 Jun 2020 09:31:55 GMT
• Title: MUMBO: MUlti-task Max-value Bayesian Optimization
• Authors: Henry B. Moss, David S. Leslie and Paul Rayson
• Abstract summary: MUMBO is the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. We derive a novel multi-task version of entropy search, delivering robust performance with low computational overheads.
• Score: 10.10241176664951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function. This is a broad class of problems including the popular task of multi-fidelity optimization. However, while information-theoretic acquisition functions are known to provide state-of-the-art Bayesian optimization, existing implementations for multi-task scenarios have prohibitive computational requirements. Previous acquisition functions have therefore been suitable only for problems with both low-dimensional parameter spaces and function query costs sufficiently large to overshadow very significant optimization overheads. In this work, we derive a novel multi-task version of entropy search, delivering robust performance with low computational overheads across classic optimization challenges and multi-task hyper-parameter tuning. MUMBO is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces.

Related papers

• An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization [0.0]
  We propose AdaDropout to tackle high-dimensional challenges and improve solution quality. It achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches.
  arXiv  Detail & Related papers  (2025-04-15T16:23:25Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization [37.339567743948955]
  We present a novel Bayesian optimization framework specifically tailored to address the limitations of BO. Our key contribution is a highly scalable, sample-based acquisition function that performs a non-dominated sorting of objectives. We show that our acquisition function in combination with different Bayesian neural network surrogates is effective in data-intensive environments with a minimal number of iterations.
  arXiv  Detail & Related papers  (2023-06-01T19:10:57Z)
• Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks [3.0468934705223774]
  We propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. We show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs.
  arXiv  Detail & Related papers  (2023-02-14T18:55:21Z)
• 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)
• Pre-training helps Bayesian optimization too [49.28382118032923]
  We seek an alternative practice for setting functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. Our results show that our method is able to locate good hyper parameters at least 3 times more efficiently than the best competing methods.
  arXiv  Detail & Related papers  (2022-07-07T04:42:54Z)
• Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview [10.081056751778712]
  We introduce the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. We provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.
  arXiv  Detail & Related papers  (2022-06-15T10:23:19Z)
• Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks [17.370056935194786]
  We propose Batch Multi-fidelity Bayesian Optimization with Deep Auto-Regressive Networks (BMBO-DARN) We use a set of Bayesian neural networks to construct a fully auto-regressive model, which is expressive enough to capture strong yet complex relationships across all fidelities. We develop a simple yet efficient batch querying method, without any search over fidelities.
  arXiv  Detail & Related papers  (2021-06-18T02:55:48Z)
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm [97.66038345864095]
  We propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG) Specifically, we first formulate hyperparameter optimization as an A-based constrained optimization problem. Then, we use the average zeroth-order hyper-gradients to update hyper parameters.
  arXiv  Detail & Related papers  (2021-02-17T21:03:05Z)
• Incorporating Expert Prior in Bayesian Optimisation via Space Warping [54.412024556499254]
  In big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum.
  arXiv  Detail & Related papers  (2020-03-27T06:18:49Z)
• Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization [0.1749935196721634]
  We use the addition-ality of the objective function into mapping both the kernel and the acquisition function of the Bayesian Optimization. This ap-proach makes more efficient the learning/updating of the probabilistic surrogate model. Results are presented for real-life application, that is the control of pumps in urban water distribution systems.
  arXiv  Detail & Related papers  (2020-03-09T15:45:57Z)

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.