Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space

• URL: http://arxiv.org/abs/2502.09329v1
• Date: Thu, 13 Feb 2025 13:43:52 GMT
• Title: Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
• Authors: Kazuki Ishikawa, Ryota Ozaki, Yohei Kanzaki, Ichiro Takeuchi, Masayuki Karasuyama,
• Abstract summary: A machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. Many existing studies use Bayesian optimization (BO) for accelerating the search. Our proposed method embeds different hyper- parameter spaces into a shared latent space, in which a surrogate multi-task model for BO is estimated. This approach can share information of observations from different ML algorithms by which efficient optimization is expected with a smaller number of total observations.
• Score: 16.257223975129513
• License:
• Abstract: Selecting the optimal combination of a machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. However, since the combination of ML algorithms and hyper-parameters is enormous, the exhaustive validation requires a significant amount of time. Many existing studies use Bayesian optimization (BO) for accelerating the search. On the other hand, a significant difficulty is that, in general, there exists a different hyper-parameter space for each one of candidate ML algorithms. BO-based approaches typically build a surrogate model independently for each hyper-parameter space, by which sufficient observations are required for all candidate ML algorithms. In this study, our proposed method embeds different hyper-parameter spaces into a shared latent space, in which a surrogate multi-task model for BO is estimated. This approach can share information of observations from different ML algorithms by which efficient optimization is expected with a smaller number of total observations. We further propose the pre-training of the latent space embedding with an adversarial regularization, and a ranking model for selecting an effective pre-trained embedding for a given target dataset. Our empirical study demonstrates effectiveness of the proposed method through datasets from OpenML.

Related papers

• Modeling All Response Surfaces in One for Conditional Search Spaces [69.90317997694218]
  This paper proposes a novel approach to model the response surfaces of all subspaces in one. We introduce an attention-based deep feature extractor, capable of projecting configurations with different structures from various subspaces into a unified feature space.
  arXiv  Detail & Related papers  (2025-01-08T03:56:06Z)
• Efficient Multi-agent Reinforcement Learning by Planning [33.51282615335009]
  Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. We propose the MAZero algorithm, which combines a centralized model with Monte Carlo Tree Search (MCTS) for policy search.
  arXiv  Detail & Related papers  (2024-05-20T04:36:02Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel [20.98449975854329]
  This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper- parameters. The newly proposed grid spectral mixture product (GSMP) kernel is tailored for multi-dimensional data. To exploit the inherent sparsity of the solutions, we introduce the Sparse LInear Multiple Kernel Learning (SLIM-KL) framework.
  arXiv  Detail & Related papers  (2023-09-15T07:05:33Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Multi-objective hyperparameter optimization with performance uncertainty [62.997667081978825]
  This paper presents results on multi-objective hyperparameter optimization with uncertainty on the evaluation of Machine Learning algorithms. We combine the sampling strategy of Tree-structured Parzen Estimators (TPE) with the metamodel obtained after training a Gaussian Process Regression (GPR) with heterogeneous noise. Experimental results on three analytical test functions and three ML problems show the improvement over multi-objective TPE and GPR.
  arXiv  Detail & Related papers  (2022-09-09T14:58:43Z)
• A model aggregation approach for high-dimensional large-scale optimization [2.1104930506758275]
  We propose a model aggregation method in the Bayesian optimization (MamBO) algorithm for efficiently solving high-dimensional large-scale optimization problems. MamBO uses a combination of subsampling and subspace embeddings to collectively address high dimensionality and large-scale issues. Our proposed model aggregation method reduces these lower-dimensional surrogate model risks and improves the robustness of the BO algorithm.
  arXiv  Detail & Related papers  (2022-05-16T08:58:42Z)
• Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning [0.0]
  In reinforcement learning (RL), the information content of data gathered by the learning agent is dependent on the setting of many hyper- parameters. In this work, a novel approach for autonomous hyper- parameter setting using Bayesian optimization is proposed. Experiments reveal promising results compared to other manual tweaking and optimization-based approaches.
  arXiv  Detail & Related papers  (2021-12-15T13:10:44Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization [1.6114012813668934]
  We propose HyP-ABC, an automatic hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned.
  arXiv  Detail & Related papers  (2021-09-11T16:45:39Z)

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.