Practical and sample efficient zero-shot HPO

• URL: http://arxiv.org/abs/2007.13382v1
• Date: Mon, 27 Jul 2020 08:56:55 GMT
• Title: Practical and sample efficient zero-shot HPO
• Authors: Fela Winkelmolen, Nikita Ivkin, H. Furkan Bozkurt, Zohar Karnin
• Abstract summary: We provide an overview of available approaches and introduce two novel techniques to handle the problem. The first is based on a surrogate model and adaptively chooses pairs of dataset, configuration to query. The second is for settings where finding, tuning and testing a surrogate model is problematic, is a multi-fidelity technique combining HyperBand with submodular optimization.
• Score: 8.41866793161234
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement each other. That is to say, for any given dataset, at least one of them is expected to perform well. Current techniques for obtaining this list are computationally expensive as they rely on running training jobs on a diverse collection of datasets and a large collection of randomly drawn HPs. This cost is especially problematic in environments where the space of HPs is regularly changing due to new algorithm versions, or changing architectures of deep networks. We provide an overview of available approaches and introduce two novel techniques to handle the problem. The first is based on a surrogate model and adaptively chooses pairs of dataset, configuration to query. The second, for settings where finding, tuning and testing a surrogate model is problematic, is a multi-fidelity technique combining HyperBand with submodular optimization. We benchmark our methods experimentally on five tasks (XGBoost, LightGBM, CatBoost, MLP and AutoML) and show significant improvement in accuracy compared to standard zero-shot HPO with the same training budget. In addition to contributing new algorithms, we provide an extensive study of the zero-shot HPO technique resulting in (1) default hyper-parameters for popular algorithms that would benefit the community using them, (2) massive lookup tables to further the research of hyper-parameter tuning.

Related papers

• Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset [0.15420205433587747]
  We present a two-step HPO method as a strategic solution to curbing computational demands and wait times. We present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation.
  arXiv  Detail & Related papers  (2023-02-08T02:38:26Z)
• 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)
• 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)
• AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [72.54359545547904]
  We propose a gradient-based subset selection framework for hyper- parameter tuning. We show that using gradient-based data subsets for hyper- parameter tuning achieves significantly faster turnaround times and speedups of 3$times$-30$times$.
  arXiv  Detail & Related papers  (2022-03-15T19:25:01Z)
• 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)
• Genealogical Population-Based Training for Hyperparameter Optimization [1.0514231683620516]
  We experimentally demonstrate that our method cuts down by 2 to 3 times the computational cost required. Our method is search-algorithm so that the inner search routine can be any search algorithm like TPE, GP, CMA or random search.
  arXiv  Detail & Related papers  (2021-09-30T08:49:41Z)
• 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)
• Cost-Efficient Online Hyperparameter Optimization [94.60924644778558]
  We propose an online HPO algorithm that reaches human expert-level performance within a single run of the experiment. Our proposed online HPO algorithm reaches human expert-level performance within a single run of the experiment, while incurring only modest computational overhead compared to regular training.
  arXiv  Detail & Related papers  (2021-01-17T04:55:30Z)
• Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules [0.6875312133832078]
  We build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets. We use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper- parameter search space to select a better combination.
  arXiv  Detail & Related papers  (2020-06-03T08:53:48Z)
• HyperSTAR: Task-Aware Hyperparameters for Deep Networks [52.50861379908611]
  HyperSTAR is a task-aware method to warm-start HPO for deep neural networks. It learns a dataset (task) representation along with the performance predictor directly from raw images. It evaluates 50% less configurations to achieve the best performance compared to existing methods.
  arXiv  Detail & Related papers  (2020-05-21T08:56:50Z)

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.