BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering

• URL: http://arxiv.org/abs/2507.20446v2
• Date: Thu, 07 Aug 2025 17:12:27 GMT
• Title: BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
• Authors: Guanghui Zhu, Xin Fang, Feng Cheng, Lei Wang, Wenzhong Chen, Chunfeng Yuan, Yihua Huang,
• Abstract summary: We propose a combined Bayesian Optimization and Adaptive Successive Filtering algorithm (BOASF) under a unified multi-armed bandit framework.<n>BOASF consists of multiple evaluation rounds in each of which we select promising configurations for each arm.<n>A Softmax model is employed to adaptively allocate available resources for each promising arm that advances to the next round.
• Score: 19.936995666636484
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning task successfully and efficiently. Finding the optimal machine learning model or the hyperparameter combination set from a large number of possible alternatives usually requires considerable expert knowledge and experience. To tackle this problem, we propose a combined Bayesian Optimization and Adaptive Successive Filtering algorithm (BOASF) under a unified multi-armed bandit framework to automate the model selection or the hyperparameter optimization. Specifically, BOASF consists of multiple evaluation rounds in each of which we select promising configurations for each arm using the Bayesian optimization. Then, ASF can early discard the poor-performed arms adaptively using a Gaussian UCB-based probabilistic model. Furthermore, a Softmax model is employed to adaptively allocate available resources for each promising arm that advances to the next round. The arm with a higher probability of advancing will be allocated more resources. Experimental results show that BOASF is effective for speeding up the model selection and hyperparameter optimization processes while achieving robust and better prediction performance than the existing state-of-the-art automatic machine learning methods. Moreover, BOASF achieves better anytime performance under various time budgets.

Related papers

• "FRAME: Forward Recursive Adaptive Model Extraction-A Technique for Advance Feature Selection" [0.0]
  This study introduces a novel hybrid approach, the Forward Recursive Adaptive Model Extraction Technique (FRAME)<n>FRAME combines Forward Selection and Recursive Feature Elimination to enhance feature selection across diverse datasets.<n>The results demonstrate that FRAME consistently delivers superior predictive performance based on downstream machine learning evaluation metrics.
  arXiv  Detail & Related papers  (2025-01-21T08:34:10Z)
• Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
  We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives.<n>We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
  arXiv  Detail & Related papers  (2024-10-29T14:41:44Z)
• Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification [0.01649298969786889]
  We investigate the potential of randomly generated functions (RGF) for the model training. We focus on automated algorithm configuration (AAC) We analyze the performance of dense neural network (NN) models in handling the mixed regression and classification tasks.
  arXiv  Detail & Related papers  (2024-09-02T20:04:41Z)
• 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)
• 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)
• 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)
• Adaptive Local Bayesian Optimization Over Multiple Discrete Variables [9.860437640748113]
  This paper describes the approach of team KAIST OSI in a step-wise manner, which outperforms the baseline algorithms by up to +20.39%. In a similar vein, we combine the methodology of Bayesian and multi-armed bandit,(MAB) approach to select the values with the consideration of the variable types. Empirical evaluations demonstrate that our method outperforms the existing methods across different tasks.
  arXiv  Detail & Related papers  (2020-12-07T07:51:23Z)
• Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
  We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
  arXiv  Detail & Related papers  (2020-08-02T02:56:30Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

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.