Bayesian optimisation for Intelligent Environmental Monitoring

By Román Marchant, Fabio Tozeto Ramos. 2012

192 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Science Applications

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. Frank Hutter
2. Nando de Freitas
3. Ziyun Wang
4. Sergey Levine
5. Quoc V. Le
6. Aaron Klein
7. Kevin Swersky
8. Pieter Abbeel

In Computational Theory and Mathematics

1. Frank Hutter
2. Nando de Freitas
3. Ziyun Wang
4. Ruben Martinez-Cantin
5. Aaron Klein
6. Bobak Shahriari
7. Ryan P. Adams
8. Matthew W. Hoffman

In Computer Science Applications

1. Ruben Martinez-Cantin
2. Nando de Freitas
3. Bobak Shahriari
4. Ziyun Wang
5. Masrour Zoghi

Citations in the corpus

• Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Fortunately, it does not depend on w and is therefore simply a normalizing constant.”
• BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014, methodology): “This can be used to optimize a function considering an additional cost for each sample, for example a moving sensor (Marchant and Ramos, 2012).”
• Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013, background): “…Martinez-Cantin et al., 2007; Lizotte et al., 2007; Frazier et al., 2009; Azimi et al., 2010; Hamze et al., 2011; Azimi et al., 2011; Bergstra et al., 2011; Gramacy & Polson, 2011; Denil et al., 2012; Mahendran et al., 2012; Azimi et al., 2012; Hen-nig & Schuler, 2012; Marchant & Ramos, 2012).”
• Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters (2014, background): “Bayesian optimisation has become an important area of research and development in the field of machine learning, as evidenced by recent media coverage [8] and a blossoming range of applications to interactive user-interfaces [4], robotics [16, 19], environmental monitoring [18], information extracti”
• An Entropy Search Portfolio for Bayesian Optimization (2014, methodology): “Major applications of these techniques include interactive user-interfaces [6, 4], robotics [18, 21], environmental monitoring [20], information extraction [33], combinatorial optimization [16, 34], sensor networks [10, 28], adaptive Monte Carlo [19, 32], experimental design [1], and reinforcement…”
• Heteroscedastic Treed Bayesian Optimisation (2014, background): “, 2009), environmental monitoring (Marchant and Ramos, 2012), estimating thermophysical properties of materials (Assael et al.”