Easy Hyperparameter Search Using Optunity

By Marc Claesen, Jaak Simm, D. Popovic, Yves Moreau, B. De Moor. 2014

86 citations recorded by the source corpus.

Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments. Optunity is written in Python and contains interfaces to environments such as R and MATLAB. Optunity uses a BSD license and is freely available online at this http URL

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Li Yang
2. Abdallah Shami
3. Yong Li
4. Chen Gao
5. Xiangnan He
6. Jian-Guang Lou
7. Bei Chen
8. Yihong Chen

In Computational Theory and Mathematics

1. Li Yang
2. Abdallah Shami
3. Marc-André Zöller
4. B. De Moor
5. Marco F. Huber
6. Jian-Guang Lou
7. Marc Claesen
8. Xiangnan He

Citations in the corpus

• Survey on Automated Machine Learning (2019, background): “Optunity (Claesen et al., 2014) is a generic framework for CASH with a set of different solvers.”
• Named Entity Recognition with Bidirectional LSTM-CNNs (2015, methodology): “24 For the second round, we performed independent hyper-parameter searches on each dataset using Optunity’s implementation of particle swarm (Claesen et al., 2014), as there is some evidence that it is more efficient than random search (Clerc and Kennedy, 2002).”
• Hyperparameter Search in Machine Learning (2015, background): “These approaches leave much to be desired in terms of reproducibility and are impractical when the number of hyperparameters is large [10].”
• λOpt: Learn to Regularize Recommender Models in Finer Levels (2019, background): “Previous work [11, 21, 28, 30, 31] have dedicated to lower down the nontrivial search cost, along with developing some enhanced toolboxes [4, 7, 23].”
• Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results (2021, background): “This exponential progress is known as the dimensionality curse [25].”
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “Optunity [83] is a popular HPO framework that provides several optimization techniques, including GS, RS, PSO, and BO-TPE.”