BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits

By Ruben Martinez-Cantin. 2014

317 citations recorded by the source corpus.

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.

How researchers see this paper

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

1. Chen Gao
2. Li Yang
3. Yong Li
4. Abdallah Shami
5. Radwa El Shawi
6. Yihong Chen
7. Bei Chen
8. Xiangnan He

Citations in the corpus

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019, positive): “Gaussian processes have become the standard surrogate for modeling the objective function in Bayesian optimization [112, 80].”
• High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates (2017, methodology): “The second advantage of Bayesian optimization, as opposed to gradient-based optimization, is that it uses all previous measurements to inform the next step in the optimization process, resulting in an efficient use of the collected data [6].”
• Easy Hyperparameter Search Using Optunity (2014, methodology): “Some packages dedicated to Bayesian approaches include Spearmint (Snoek et al., 2012), DiceKriging (Roustant et al., 2012) and BayesOpt (Martinez-Cantin, 2014).”
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “ Gaussian process as the surrogate model. Spearmint’s primary deciency is that it is not very ecient for categorical and conditional hyper-parameters. 6.3. BayesOpt Bayesian Optimization (BayesOpt) [109] is a Python library employed to solve HPO problems using BO. BayesOpt uses a Gaussian process as”
• A Stratified Analysis of Bayesian Optimization Methods (2016, methodology): “Numerous strategies for modeling f in the Bayesian optimization setting have been suggested, including the use of Gaussian processes (Snoek et al., 2012; Martinez-Cantin, 2014), random forests (Hutter et al., 2011b), and treestructured Parzen estimators (Bergstra et al., 2011; 2013b).”
• Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees (2015, methodology): “We use a global optimization library (Martinez-Cantin, 2014) to automatically select the best set of parameters.”
• Bayesian Optimization for Adaptive Experimental Design: A Review (2020, background): “incrementally as new points arrive, reducing the complexity to O(n2) [123].”
• 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].”