Parameter space exploration with Gaussian process trees

By Robert B. Gramacy, Herbert K. H. Lee, William G. Macready. 2004

82 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

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. Nando de Freitas
2. Frank Hutter
3. Ziyun Wang
4. Sergey Levine
5. Kevin Swersky
6. Quoc V. Le
7. Ryan P. Adams
8. Yoshua Bengio

In Computational Theory and Mathematics

1. Nando de Freitas
2. Ziyun Wang
3. Frank Hutter
4. Ryan P. Adams
5. Bobak Shahriari
6. Kevin Swersky
7. Daniel W. Apley
8. Masrour Zoghi

In Management Science and Operations Research

1. Ziyun Wang
2. Nando de Freitas
3. Bobak Shahriari
4. Masrour Zoghi
5. Frank Hutter
6. Daniel W. Apley
7. Kevin Swersky
8. Ryan P. Adams

Citations in the corpus

• Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “[63] proposed the treed GP model, which partitions the data and then applies a separate GP to each region.”
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling (2007, methodology): “…process model for the rocket booster computer ex-periment, but it also has a wide range of uses as a simple and efficient method for We believe that a large contribution of the treed GP will be in the domain of sequential design of computer experiments (Santner et al., 2003; Gramacy et al., 2004).”
• A non‐stationary covariance‐based Kriging method for metamodelling in engineering design (2007, methodology): “[24] presented an approach that utilizes Gaussian process trees to implement the nonstationary Gaussian process.”
• Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (2012, background): “We refer the reader to (Močkus, 1982; Schonlau et al., 1998; Gramacy et al., 2004; Brochu et al., 2007; Lizotte, 2008; Martinez–Cantin et al., 2009; Garnett et al., 2010) for practical examples.”
• Heteroscedastic Treed Bayesian Optimisation (2014, background): “Whereas other treed GP models propose partitioning the data for computational efficiency (Gramacy et al., 2004; Bui and Turner, 2014) or capturing multiple scales in the data (Dunson and Fox, 2012), our approach is designed to learn non-stationarity in the random process that generated the objective”
• Adaptive MCMC with Bayesian Optimization (2012, methodology): “We refer readers to Gramacy et al. (2004) and Brochu et al. (2009) for examples.”