Hyperparameter Optimization via Sequential Uniform Designs

• URL: http://arxiv.org/abs/2009.03586v2
• Date: Thu, 17 Jun 2021 09:12:26 GMT
• Title: Hyperparameter Optimization via Sequential Uniform Designs
• Authors: Zebin Yang and Aijun Zhang
• Abstract summary: This paper reformulates HPO as a computer experiment and proposes a novel sequential uniform design (SeqUD) strategy with three-fold advantages. The proposed SeqUD strategy outperforms benchmark HPO methods, and it can be therefore a promising and competitive alternative to existing AutoML tools.
• Score: 4.56877715768796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generally unknown, hence essentially a global optimization problem. This paper reformulates HPO as a computer experiment and proposes a novel sequential uniform design (SeqUD) strategy with three-fold advantages: a) the hyperparameter space is adaptively explored with evenly spread design points, without the need of expensive meta-modeling and acquisition optimization; b) the batch-by-batch design points are sequentially generated with parallel processing support; c) a new augmented uniform design algorithm is developed for the efficient real-time generation of follow-up design points. Extensive experiments are conducted on both global optimization tasks and HPO applications. The numerical results show that the proposed SeqUD strategy outperforms benchmark HPO methods, and it can be therefore a promising and competitive alternative to existing AutoML tools.

Related papers

• 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)
• Deep Ranking Ensembles for Hyperparameter Optimization [9.453554184019108]
  We present a novel method that meta-learns neural network surrogates optimized for ranking the configurations' performances while modeling their uncertainty via ensembling. In a large-scale experimental protocol comprising 12 baselines, 16 HPO search spaces and 86 datasets/tasks, we demonstrate that our method achieves new state-of-the-art results in HPO.
  arXiv  Detail & Related papers  (2023-03-27T13:52:40Z)
• Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization [0.0]
  This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyper- parameters in a machine learning model. The behavior of the presented model, specifically against the changes in its design parameters, is investigated in both machine learning and global function optimization applications.
  arXiv  Detail & Related papers  (2023-03-03T21:10:17Z)
• 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)
• 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)
• Towards Learning Universal Hyperparameter Optimizers with Transformers [57.35920571605559]
  We introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction. Our experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates.
  arXiv  Detail & Related papers  (2022-05-26T12:51:32Z)
• A Comparative study of Hyper-Parameter Optimization Tools [2.6097538974670935]
  We compare the performance of four python libraries, namely Optuna, Hyperopt, Optunity, and sequential model algorithm configuration (SMAC) We found that Optuna has better performance for CASH problem and NeurIPS black-box optimization challenge.
  arXiv  Detail & Related papers  (2022-01-17T14:49:36Z)
• Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges [5.139260825952818]
  This paper reviews important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.
  arXiv  Detail & Related papers  (2021-07-13T04:55:47Z)
• 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)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters [3.37314595161109]
  We propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyper- parameters. We show that the proposed approaches outperform the state-of-the-art apporaches significantly.
  arXiv  Detail & Related papers  (2020-03-03T19:26:23Z)

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.