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Random Search for Hyper-Parameter Optimization

James Bergstra, Yoshua Bengio
2012 Journal of machine learning research  
Grid search and manual search are the most widely used strategies for hyper-parameter optimization.  ...  We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline  ...  To investigate the effect of one hyper-parameter of interest X, we recommend random search (instead of grid search) for optimizing over other hyper-parameters.  ... 
dblp:journals/jmlr/BergstraB12 fatcat:p2ekjdmib5cf3aebxzmn5f3ane

A Comparative study of Hyper-Parameter Optimization Tools [article]

Shashank Shekhar, Adesh Bansode, Asif Salim
2022 arXiv   pre-print
The conventional methods for this purpose are grid search and random search and both methods create issues in industrial-scale applications.  ...  Hyper-parameter optimization (HPO) is a systematic process that helps in finding the right values for them.  ...  Random Search Random search is another commonly used approach in which the hyper-parameters are selected at random, independent of other choices.  ... 
arXiv:2201.06433v1 fatcat:6zbmi34kdbcc3kundgu52g5rka

Algorithms for Hyper-Parameter Optimization

James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
2011 Neural Information Processing Systems  
We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion.  ...  Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs.  ...  One simple, but recent step toward formalizing hyper-parameter optimization is the use of random search [5] . [19] showed that random search was much more efficient than grid search for optimizing the  ... 
dblp:conf/nips/BergstraBBK11 fatcat:jwye6abnnfeeneqh3owfugflgq

The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms

Guo, Hu, Wu, Peng, Wu
2019 Electronics  
In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning  ...  Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization.  ...  Larochelle et al. proposed a Grid Search [5] method for hyper-parameter optimization.  ... 
doi:10.3390/electronics8050579 fatcat:xydj7zyao5cibcdkmgyxpbscjm

Making a Science of Model Search [article]

J. Bergstra and D. Yamins and D. D. Cox
2012 arXiv   pre-print
A hyper parameter optimization algorithm transforms this graph into a program for optimizing that performance metric.  ...  In this work, we propose a meta-modeling approach to support automated hyper parameter optimization, with the goal of providing practical tools to replace hand-tuning with a reproducible and unbiased optimization  ...  We compared random search in that model class with a more sophisticated algorithm for hyper parameter optimization, and found that the optimization-based search strategy recovered or improved on the best  ... 
arXiv:1209.5111v1 fatcat:nxnbsjvurnhzjc6yycqh7yyrem

Rethinking Performance Estimation in Neural Architecture Search [article]

Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian
2020 arXiv   pre-print
Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration.  ...  Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach.  ...  Hyper-parameter Optimization Hyper-parameter optimization [41] aims to automatically optimize the hyper-parameters during the learning process [4, 19, 39, 45] .  ... 
arXiv:2005.09917v1 fatcat:6g4csdl55nd2zmb3qg3o7e6koi

Rethinking Performance Estimation in Neural Architecture Search

Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration.  ...  Since searching an optimal BPE is extremely timeconsuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach.  ...  Hyper-parameter Optimization Hyper-parameter optimization [41] aims to automatically optimize the hyper-parameters during the learning process [4, 19, 39, 45] .  ... 
doi:10.1109/cvpr42600.2020.01137 dblp:conf/cvpr/ZhengJWYL0020 fatcat:fovng42u4zbqhl73gy6xojqbsu

Image Classification Based on KPCA and SVM with Randomized Hyper-parameter Optimization

Lin Li, Jin Lian, Yue Wu, Mao Ye
2014 International Journal of Signal Processing, Image Processing and Pattern Recognition  
At the third step we use KPCA for feature dimensionality reduction. Finally we classify image by SVM with randomized hyper-parameter optimization.  ...  on kernel principal component analysis(KPCA) for feature descriptors post-processing and support vector machine (SVM) with randomized hyper-parameter optimization for classification.  ...  At the classification step, the running time of hyper-parameter with randomized search is about ten times as fast as that of hyper-parameter with grid search while keeping the total accuracy.  ... 
doi:10.14257/ijsip.2014.7.4.29 fatcat:qwhjvuot7vetpdhr5i4t3mwbee

Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model [chapter]

Usman Sammani Sani, Daphne Teck Ching Lai, Owais Ahmed Malik
2021 Frontiers in Artificial Intelligence and Applications  
To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques.  ...  For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered.  ...  The traditional way of hyper-parameter tuning is either through Grid search or Random search.  ... 
doi:10.3233/faia210413 fatcat:6xa6yqcj4jhndg7bwmkpg7cqzu

Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning [article]

Xiang Zhang, Xiaocong Chen, Lina Yao, Chang Ge, Manqing Dong
2020 arXiv   pre-print
The proposed method is compared to the state-of-the-art hyper-parameter tuning methods including manually (e.g., grid search and random search) and automatically (e.g., Bayesian Optimization) ones.  ...  Addressing the above issue, this paper presents an efficient Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning.  ...  Grid search and random search require all possible values for each parameter whereas Bayesian optimization needs the range for each parameter.  ... 
arXiv:1907.13359v2 fatcat:axzzzmzchvhfjiba6te2i5hzgm

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [article]

Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti, Alexandre V. Evfimievski, Lucian Popa, Ganesh Ramakrishnan, Rishabh Iyer
2022 arXiv   pre-print
Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly  ...  over the entire dataset for different possible sets of hyper-parameters.  ...  D.1 Random Search In random search [42] , hyper-parameter configurations are selected at random and evaluated to discover the optimal configuration among those chosen.  ... 
arXiv:2203.08212v1 fatcat:4qhlvd32hndmzh4yifhmnxvb6q

A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer [article]

Gabriel Maher, Stephen Boyd, Mykel Kochenderfer, Cristian Matache, Alex Ulitsky, Slava Yukhymuk, Leonid Kopman
2022 arXiv   pre-print
We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives  ...  We focus on the common scenario where there are on the order of tens of hyper-parameters, each with various attributes such as a range of continuous values, or a finite list of values, and whether it should  ...  Alternatively hyper-parameter values could also be sampled from the hyper-parameter search space uniformly at random and evaluated [3] .  ... 
arXiv:2202.07735v1 fatcat:ioaubojxafe37dkclbzcjo3boe

Application of Random Forest Regression with Hyper-parameters Tuning to Estimate Reference Evapotranspiration

Satendra Kumar Jain, Anil Kumar Gupta
2022 International Journal of Advanced Computer Science and Applications  
These hyper-parameters are applied in three different ways to the models such as one hyper-parameter parameter at a time, and combination of hyper-parameters using grid search, and random search approaches  ...  This study also reveals that the model that optimises the hyper-parameters using a grid search approach shows equal predictive power but takes much execution time whereas random search based optimization  ...  These hyper-parameters are optimized and applied in three different ways to the models such as one parameter at a time, and combinations of hyper parameters using grid search, and random search.  ... 
doi:10.14569/ijacsa.2022.0130585 fatcat:ni4miej25bgsvkgeedewjobmgy

Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Ananto Setyo Wicaksono, Ahmad Afif
2018 International Journal of Advanced Computer Science and Applications  
Determining the hyper parameter can be time consuming if we use grid search method because grid search is a method which tries all possible combination of hyper parameter.  ...  The result of implementation shows that genetic algorithm can get the hyper parameter with almost the same result with grid search with faster computational time.  ...  method for hyper parameter optimization [16] .  ... 
doi:10.14569/ijacsa.2018.091238 fatcat:xcibcolozfeezgv54ommmjx2ma

Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules [article]

Michele Fraccaroli, Evelina Lamma, Fabrizio Riguzzi
2020 arXiv   pre-print
The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization.  ...  the hyper-parameter search space to select a better combination.  ...  Random Search uses the same hyper-parameter space of Grid Search, but replaces the brute-force search with random search.  ... 
arXiv:2006.02105v1 fatcat:36b6swmq4ncf5p62rwnayicnu4
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